Awesome-FL

Federated Learning Resources

---

Table of Contents

• Papers
  • FL in top-tier journal
  • FL in top-tier conference and journal by category
    • AI ML DM Secure CV NLP IR DB Network System Others
  • FL on Graph Data and Graph Neural Networks [dblp]
  • FL on Tabular Data [dblp]
• Framework
• Datasets
• Surveys
• Tutorials and Courses
• Key Conferences/Workshops/Journals
  • Workshops Special Issues Special Tracks
• Update log
• Acknowledgments
• Citation

We use another project to automatically track updates to FL papers, click on FL-paper-update-tracker if you need it.

Please note that if this page does not display the full content, please visit the official homepage for full information.

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (im.young@foxmail.com). If you want to communicate with more friends in the field of federated learning, please join the QQ group [联邦学习交流群], the group number is 833638275. Enjoy reading!

Repository Update Notice

2024/09/30

Dear Users, We would like to inform you of a few changes that will affect this open source repository. The owner and principal contributor @youngfish42 has successfully completed his doctoral studies 🎓 as of September 30, 2024, and has since shifted his research focus. This change in circumstances will impact the frequency and extent of updates to the repository's paper list.

Instead of the previous regular updates, we anticipate that the paper list will now be updated on a monthly or quarterly basis. Furthermore, the depth of these updates will be reduced. For instance, updates related to the author's institution and open source code will no longer be actively maintained.

We understand that this might affect the value you derive from this repository. Therefore, we humbly invite more contributors to participate in updating the content. This collaborative effort will ensure that the repository remains a valuable resource for everyone.

We appreciate your understanding and look forward to your continued support and contributions.

Best Regards,

白小鱼 (youngfish)

papers

categories

• Artificial Intelligence (IJCAI, AAAI, AISTATS, ALT, AI)

• Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI, Machine Learning, JMLR, TPAMI)

• Data Mining (KDD, WSDM)

• Secure (S&P, CCS, USENIX Security, NDSS)

• Computer Vision (ICCV, CVPR, ECCV, MM, IJCV)

• Natural Language Processing (ACL, EMNLP, NAACL, COLING)

• Information Retrieval (SIGIR)

• Database (SIGMOD, ICDE, VLDB)

• Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)

• System (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)

• Others (ICSE, FOCS, STOC)

Events

Venue	2024-2020	before 2020
IJCAI	25, 24, 23, 22, 21, 20	19
AAAI	25, 24, 23, 22, 21, 20	-
AISTATS	25, 24, 23, 22, 21, 20	-
ALT	22	-
AI (J)	25, 23	-
NeurIPS	24, 23, 22, 21, 20	18, 17
ICML	25, 24, 23, 22, 21, 20	19
ICLR	25, 24, 23, 22, 21, 20	-
COLT	23	-
UAI	25, 24, 23, 22, 21	-
Machine Learning (J)	25, 24, 23, 22	-
JMLR (J)	24, 23, 22	-
TPAMI (J)	25, 24, 23, 22	-
KDD	25, 24, 23, 22, 21, 20
WSDM	25, 24, 23, 22, 21	19
S&P	25, 24, 23, 22	19
CCS	24, 23, 22, 21, 19	17
USENIX Security	23, 22, 20	-
NDSS	25, 24, 23, 22, 21	-
CVPR	25, 24, 23, 22, 21	-
ICCV	23,21	-
ECCV	24, 22, 20	-
MM	24, 23, 22, 21, 20	-
IJCV (J)	25, 24	-
ACL	25, 24, 23, 22, 21	19
NAACL	24, 22, 21	-
EMNLP	24, 23, 22, 21, 20	-
COLING	25, 20	-
SIGIR	25, 24, 23, 22, 21, 20	-
SIGMOD	22, 21	-
ICDE	25, 24, 23, 22, 21	-
VLDB	25, 24, 23, 22, 21, 21, 20	-
SIGCOMM	25	-
INFOCOM	25, 24, 23, 22, 21, 20	19, 18
MobiCom	24, 23, 22, 21, 20
NSDI	25, 23(1, 2)	-
WWW	25, 24, 23, 22, 21
OSDI	21	-
SOSP	21	-
ISCA	24	-
MLSys	24, 23, 22, 20	19
EuroSys	25, 24, 23, 22, 21, 20
TPDS (J)	25, 24, 23, 22, 21, 20	-
DAC	25, 24, 22, 21	-
TOCS	-	-
TOS	-	-
TCAD	25, 24, 23, 22, 21	-
TC	25, 24, 23, 22, 21	-
ICSE	25, 23, 21	-
FOCS	-	-
STOC	-	-

keywords

Statistics: code is available & stars >= 100 | citation >= 50 | Top-tier venue

kg.: Knowledge Graph | data.: dataset  |   surv.: survey

fl in top-tier journal

Papers of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to WOS search engine.

fl in top-tier journal

Title	Venue	Year	Materials
Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption	Nat. Mach. Intell.	2025	[PUB] [PDF] [CODE]
Incentivizing inclusive contributions in model sharing markets	Nat. Commun.	2025	[PUB] [CODE]
FedECA: federated external control arms for causal inference with time-to-event data in distributed settings	Nat. Commun.	2025	[PUB] [CODE]
Privacy-preserving multicenter differential protein abundance analysis with FedProt	Nat. Comput. Sci.	2025	[PUB] [CODE]
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge	Nat. Commun.	2025	[PUB] [CODE]
A fully open AI foundation model applied to chest radiography	Nature	2025	[PUB] [CODE]
Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator	Nat. Electron.	2025	[PUB]
A framework reforming personalized Internet of Things by federated meta-learning	Nat. Commun.	2025	[PUB] [CODE]
Achieving flexible fairness metrics in federated medical imaging	Nat. Commun.	2025	[PUB] [CODE]
Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare	Nat. Commun.	2025	[PUB] [CODE]
Data-driven federated learning in drug discovery with knowledge distillation	Nat. Mach. Intell.	2025	[PUB] [CODE]
Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals	Nat. Commun.	2025	[PUB]
Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models	Nat. Commun.	2025	[PUB] [新闻]
MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing	Nat. Commun.	2024	[PUB] [CODE]
Introducing edge intelligence to smart meters via federated split learning	Nat. Commun.	2024	[PUB] [新闻]
An international study presenting a federated learning AI platform for pediatric brain tumors	Nat. Commun.	2024	[PUB] [CODE]
PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data	Science Advances	2024	[PUB] [CODE]
Federated learning is not a cure-all for data ethics	Nat. Mach. Intell.(Comment)	2024	[PUB]
Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence	Nat. Commun.	2024	[PUB] [CODE]
Selective knowledge sharing for privacy-preserving federated distillation without a good teacher	Nat. Commun.	2024	[PUB] [PDF] [CODE]
A federated learning system for precision oncology in Europe: DigiONE	Nat. Med. (Comment)	2024	[PUB]
Multi-client distributed blind quantum computation with the Qline architecture	Nat. Commun.	2023	[PUB] [PDF]
Device-independent quantum randomness–enhanced zero-knowledge proof	PNAS	2023	[PUB] [PDF] [新闻]
Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning	Nat. Commun.	2023	[PUB]
Advocating for neurodata privacy and neurotechnology regulation	Nat. Protoc. (Perspective)	2023	[PUB]
Federated benchmarking of medical artificial intelligence with MedPerf	Nat. Mach. Intell.	2023	[PUB] [PDF] [CODE]
Algorithmic fairness in artificial intelligence for medicine and healthcare	Nat. Biomed. Eng. (Perspective)	2023	[PUB] [PDF]
Differentially private knowledge transfer for federated learning	Nat. Commun.	2023	[PUB] [CODE]
Decentralized federated learning through proxy model sharing	Nat. Commun.	2023	[PUB] [PDF] [CODE]
Federated machine learning in data-protection-compliant research	Nat. Mach. Intell.(Comment)	2023	[PUB]
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer	Nat. Med.	2023	[PUB] [CODE]
Federated learning enables big data for rare cancer boundary detection	Nat. Commun.	2022	[PUB] [PDF] [CODE]
Federated learning and Indigenous genomic data sovereignty	Nat. Mach. Intell. (Comment)	2022	[PUB]
Federated disentangled representation learning for unsupervised brain anomaly detection	Nat. Mach. Intell.	2022	[PUB] [PDF] [CODE]
Shifting machine learning for healthcare from development to deployment and from models to data	Nat. Biomed. Eng. (Review Article)	2022	[PUB]
A federated graph neural network framework for privacy-preserving personalization	Nat. Commun.	2022	[PUB] [CODE] [解读]
Communication-efficient federated learning via knowledge distillation	Nat. Commun.	2022	[PUB] [PDF] [CODE]
Lead federated neuromorphic learning for wireless edge artificial intelligence	Nat. Commun.	2022	[PUB] [CODE] [解读]
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data	Sci. Rep.	2022	[PUB]
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence	Nat. Mach. Intell.	2021	[PUB] [PDF] [CODE]
Federated learning for predicting clinical outcomes in patients with COVID-19	Nat. Med.	2021	[PUB] [CODE]
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning	Nat. Mach. Intell.(Perspective)	2021	[PUB]
Swarm Learning for decentralized and confidential clinical machine learning	Nature	2021	[PUB] [CODE] [SOFTWARE] [解读]
End-to-end privacy preserving deep learning on multi-institutional medical imaging	Nat. Mach. Intell.	2021	[PUB] [CODE] [解读]
Communication-efficient federated learning	PANS.	2021	[PUB] [CODE]
Breaking medical data sharing boundaries by using synthesized radiographs	Science. Advances.	2020	[PUB] [CODE]
Secure, privacy-preserving and federated machine learning in medical imaging	Nat. Mach. Intell.(Perspective)	2020	[PUB]

fl in top ai conference and journal

Federated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including IJCAI(International Joint Conference on Artificial Intelligence), AAAI(AAAI Conference on Artificial Intelligence), AISTATS(Artificial Intelligence and Statistics), ALT(International Conference on Algorithmic Learning Theory), AI(Artificial Intelligence).

• IJCAI 2025, 2024, 2023, 2022, 2021, 2020, 2019
• AAAI 2025, 2024, 2023, 2022, 2021, 2020
• AISTATS 2025, 2024, 2023, 2022, 2021, 2020
• ALT 2022
• AI 2025, 2023

fl in top ai conference and journal

2025

IJCAI

• Exploiting Label Skewness for Spiking Neural Networks in Federated Learning. [PUB]
• FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients. [PUB]
• Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts. [PUB]
• Pixel-wise Divide and Conquer for Federated Vessel Segmentation. [PUB]
• Universal Backdoor Defense via Label Consistency in Vertical Federated Learning. [PUB]
• Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning. [PUB]
• Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning. [PUB] [COCE]
• FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks. [PUB] [CODE]
• Federated Multi-view Graph Clustering with Incomplete Attribute Imputation. [PUB]
• ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network. [PUB]
• Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning. [PUB]
• FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition. [PUB]
• FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data. [PUB]
• FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity. [PUB]
• FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [PUB]
• Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation. [PUB]
• Distilling A Universal Expert from Clustered Federated Learning. [PUB]
• CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [PUB]
• FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning. [PUB]
• Hypernetwork Aggregation for Decentralized Personalized Federated Learning. [PUB]
• Federated Domain Generalization with Decision Insight Matrix. [PUB]
• Generic Adversarial Attack Framework Against Vertical Federated Learning. [PUB]
• One-shot Federated Learning Methods: A Practical Guide. [PUB]
• Federated Learning at the Forefront of Fairness: A Multifaceted Perspective. [PUB]
• Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach. [PUB]
• Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization. [PUB]
• FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data. [PUB] [CODE]
• An Empirical Study of Federated Prompt Learning for Vision Language Model. [PUB]
• FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation. [PUB]
• Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric. [PUB]
• DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information. [PUB]
• Backdoor Attack on Vertical Federated Graph Neural Network Learning. [PUB]
• Federated Low-Rank Adaptation for Foundation Models: A Survey. [PUB]
• Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning. [PUB]
• FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization. [PUB]
• A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer. [PUB]
• Federated Stochastic Bilevel Optimization with Fully First-Order Gradients. [PUB]
• AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling. [PUB]
• Rethinking Federated Graph Learning: A Data Condensation Perspective. [PUB]
• MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation. [PUB]
• Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning. [PUB]
• Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning. [PUB]
• Inconsistency-Based Federated Active Learning. [PUB]

AISTATS

• Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. [PUB] [CODE]
• FedBaF: Federated Learning Aggregation Biased by a Foundation Model. [PUB]
• Global Group Fairness in Federated Learning via Function Tracking. [PUB] [CODE]
• On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. [PUB] [CODE]
• Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. [PUB] [CODE]
• ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. [PUB] [CODE]
• Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. [PUB] [CODE]
• The cost of local and global fairness in Federated Learning. [PUB] [CODE]
• Federated Communication-Efficient Multi-Objective Optimization. [PUB] [CODE]
• Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. [PUB] [CODE]
• Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. [PUB] [CODE]
• On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. [PUB] [CODE]
• DPFL: Decentralized Personalized Federated Learning. [PUB] [CODE]

AI

• FedHM: Efficient federated learning for heterogeneous models via low-rank factorization. [PUB]

AAAI

• Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval. [PUB]
• Single-Loop Federated Actor-Critic across Heterogeneous Environments. [PUB]
• Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update. [PUB]
• FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning. [PUB]
• FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation. [PUB]
• ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning. [PUB]
• Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning. [PUB]
• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective. [PUB]
• Asynchronous Federated Clustering with Unknown Number of Clusters. [PUB]
• Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval. [PUB]
• HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning. [PUB]
• LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning. [PUB]
• Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach. [PUB]
• Modeling Inter-Intra Heterogeneity for Graph Federated Learning. [PUB]
• pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning. [PUB]
• First-Order Federated Bilevel Learning. [PUB]
• GAS: Generative Activation-Aided Asynchronous Split Federated Learning. [PUB]
• FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis. [PUB]
• Federated Graph Condensation with Information Bottleneck Principles. [PUB]
• A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract). [PUB]
• Federated Learning with Sample-level Client Drift Mitigation. [PUB]
• Pilot: Building the Federated Multimodal Instruction Tuning Framework. [PUB]
• Flexible Sharpness-Aware Personalized Federated Learning. [PUB]
• MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay. [PUB]
• PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers. [PUB]
• Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs. [PUB]
• Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens. [PUB]
• Federated Recommendation with Explicitly Encoding Item Bias. [PUB]
• Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning. [PUB]
• Decentralized Federated Learning with Model Caching on Mobile Agents. [PUB]
• Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning. [PUB]
• FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement. [PUB]
• Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning. [PUB]
• Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness. [PUB]
• Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt. [PUB]
• Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach. [PUB]
• Reputation-aware Revenue Allocation for Auction-based Federated Learning. [PUB]
• Learn How to Query from Unlabeled Data Streams in Federated Learning. [PUB]
• Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract). [PUB]
• Graph Consistency and Diversity Measurement for Federated Multi-View Clustering. [PUB]
• WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling. [PUB]
• Label-Free Backdoor Attacks in Vertical Federated Learning. [PUB]
• Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection. [PUB]
• FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning. [PUB]
• Fair Federated Survival Analysis. [PUB]
• Federated t-SNE and UMAP for Distributed Data Visualization. [PUB]
• Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data. [PUB]
• Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients. [PUB]
• In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. [PUB]
• Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning. [PUB]
• Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [PUB]
• Federated Unlearning with Gradient Descent and Conflict Mitigation. [PUB]
• Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation. [PUB]
• FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update. [PUB]
• FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization. [PUB]
• Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation. [PUB]
• FedCross: Intertemporal Federated Learning Under Evolutionary Games. [PUB]
• Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning. [PUB]
• SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning. [PUB]
• Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach. [PUB]
• Federated Graph-Level Clustering Network. [PUB]
• LiD-FL: Towards List-Decodable Federated Learning. [PUB]
• Convergence Analysis of Federated Learning Methods Using Backward Error Analysis. [PUB]
• Progressive Distribution Matching for Federated Semi-Supervised Learning. [PUB]
• TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization. [PUB]
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. [PUB]
• EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning. [PUB]
• FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning. [PUB]
• pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning. [PUB]
• FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning. [PUB]
• FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning. [PUB]
• Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model. [PUB]
• PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning. [PUB]
• TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning. [PUB]
• FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning. [PUB]
• DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework. [PUB]
• Federated Assemblies. [PUB]
• Federated Causally Invariant Feature Learning. [PUB]
• A New Federated Learning Framework Against Gradient Inversion Attacks. [PUB]
• Exploring Vacant Classes in Label-Skewed Federated Learning. [PUB]
• Capture Global Feature Statistics for One-Shot Federated Learning. [PUB]
• Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning. [PUB]
• MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark. [PUB]
• Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning. [PUB]
• Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data. [PUB]
• Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization. [PUB]
• SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning. [PUB]
• Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning. [PUB]
• How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?. [PUB]
• Attribute Inference Attacks for Federated Regression Tasks. [PUB]
• Federated Binary Matrix Factorization Using Proximal Optimization. [PUB]
• Creating Coherence in Federated Non-Negative Matrix Factorization. [PUB]
• Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks. [PUB]
• DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game. [PUB]
• Federated Foundation Models on Heterogeneous Time Series. [PUB]
• FedPop: Federated Population-based Hyperparameter Tuning. [PUB]
• Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy. [PUB]
• EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity. [PUB]
• Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. [PUB]

2024

IJCAI

• Federated Multi-View Clustering via Tensor Factorization. [PUB]
• Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means. [PUB]
• LG-FGAD: An Effective Federated Graph Anomaly Detection Framework. [PUB]
• Federated Prompt Learning for Weather Foundation Models on Devices. [PUB]
• Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation. [PUB]
• Unlearning during Learning: An Efficient Federated Machine Unlearning Method. [PUB]
• Practical Hybrid Gradient Compression for Federated Learning Systems. [PUB]
• Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection. [PUB] [CODE]
• Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains. [PUB] [CODE]
• Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. [PUB]
• FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients. [PUB]
• DarkFed: A Data-Free Backdoor Attack in Federated Learning. [PUB]
• Scalable Federated Unlearning via Isolated and Coded Sharding. [PUB]
• Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning. [PUB]
• Label Leakage in Vertical Federated Learning: A Survey. [PUB]
• The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence. [PUB]
• LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game. [PUB]
• EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning. [PUB]
• Knowledge Distillation in Federated Learning: A Practical Guide. [PUB]
• FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization. [PUB]
• FedPFT: Federated Proxy Fine-Tuning of Foundation Models. [PUB] [CODE]
• A Systematic Survey on Federated Semi-supervised Learning. [PUB]
• Intelligent Agents for Auction-based Federated Learning: A Survey. [PUB]
• A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning. [PUB]
• Dual Calibration-based Personalised Federated Learning. [PUB]
• Stakeholder-oriented Decision Support for Auction-based Federated Learning. [PUB]
• Redefining Contributions: Shapley-Driven Federated Learning. [PUB] [CODE]
• A Survey on Efficient Federated Learning Methods for Foundation Model Training. [PUB]
• From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching. [PUB] [CODE]
• FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning. [PUB]
• FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. [PUB]
• FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning. [PUB]
• FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise. [PUB]
• Personalized Federated Learning for Cross-City Traffic Prediction. [PUB]
• Federated Adaptation for Foundation Model-based Recommendations. [PUB]
• BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. [PUB]
• Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning. [PUB] [CODE]
• FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning. [PUB]

AISTATS

• BOBA: Byzantine-Robust Federated Learning with Label Skewness. [PUB] [PDF] [CODE]
• Federated Linear Contextual Bandits with Heterogeneous Clients. [PUB] [PDF] [CODE]
• Federated Experiment Design under Distributed Differential Privacy. [PUB] [PDF] [CODE]
• Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. [PUB] [PDF]
• Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. [PUB] [PDF]
• SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. [PUB] [PDF] [CODE]
• Compression with Exact Error Distribution for Federated Learning. [PUB] [PDF] [CODE]
• Adaptive Federated Minimax Optimization with Lower Complexities. [PUB] [PDF]
• Adaptive Compression in Federated Learning via Side Information. [PUB] [PDF] [CODE]
• On-Demand Federated Learning for Arbitrary Target Class Distributions. [PUB] [CODE]
• FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. [PUB] [PDF] [CODE]
• Queuing dynamics of asynchronous Federated Learning. [PUB] [PDF]
• Personalized Federated X-armed Bandit. [PUB] [PDF] [CODE]
• Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. [PUB] [CODE]
• Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. [PUB] [PDF]
• Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. [PUB] [PDF] [CODE]
• Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. [PUB] [PDF] [CODE]
• Analysis of Privacy Leakage in Federated Large Language Models. [PUB] [PDF] [CODE]
• Invariant Aggregator for Defending against Federated Backdoor Attacks. [PUB] [PDF] [CODE]
• Communication-Efficient Federated Learning With Data and Client Heterogeneity. [PUB] [PDF] [CODE]

AAAI

• FedMut: Generalized Federated Learning via Stochastic Mutation. [PUB]
• Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization. [PUB] [PAGE]
• No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. [PUB] [PAGE] [PDF] [CODE]
• Formal Logic Enabled Personalized Federated Learning through Property Inference. [PUB] [PDF]
• Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks. [PUB] [PAGE] [PDF] [CODE]
• FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning. [PUB] [PAGE]
• Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators. [PUB] [PAGE] [PDF]
• Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity. [PUB] [PAGE] [PDF]
• On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning. [PUB]
• FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models. [PUB] [PAGE] [PDF]
• FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting. [PUB] [PAGE] [CODE]
• Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation. [PUB] [PAGE] [PDF] [CODE]
• Exploiting Label Skews in Federated Learning with Model Concatenation. [PUB] [PAGE] [PDF] [CODE]
• Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning. [PUB] [PAGE]
• Federated Learning via Input-Output Collaborative Distillation. [PUB] [PAGE] [PDF] [CODE]
• Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. [PUB] [PAGE] [PDF] [CODE]
• FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning. [PUB] [PDF]
• FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning. [PUB] [PAGE] [PDF]
• FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing. [PUB] [PAGE] [PDF] [CODE]
• Provably Convergent Federated Trilevel Learning. [PUB] [PDF]
• Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts. [PUB] [PAGE]
• General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants. [PUB] [PAGE]
• EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning. [PUB] [PAGE] [CODE]
• FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels. [PUB] [PAGE] [PDF] [CODE]
• Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images. [PUB] [PAGE] [PDF] [CODE]
• FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. [PUB] [PDF] [CODE]
• Federated X-armed Bandit. [PUB] [PAGE] [PDF] [CODE]
• Algorithmic Foundation of Federated Learning with Sequential Data. [PUB]
• UFDA: Universal Federated Domain Adaptation with Practical Assumptions. [PUB] [PAGE] [PDF] [CODE]
• FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update. [PUB] [PAGE] [PDF]
• Language-Guided Transformer for Federated Multi-Label Classification. [PUB] [PAGE] [PDF] [CODE]
• FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers. [PUB] [PAGE] [CODE]
• Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning. [PUB] [PAGE] [CODE]
• Federated Learning with Extremely Noisy Clients via Negative Distillation. [PUB] [PAGE] [PDF] [CODE]
• FedST: Federated Style Transfer Learning for Non-IID Image Segmentation. [PUB] [PAGE] [学报] [CODE]
• PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities. [PUB]
• A Primal-Dual Algorithm for Hybrid Federated Learning. [PUB] [PAGE] [PDF]
• FedLF: Layer-Wise Fair Federated Learning. [PUB] [PAGE]
• Towards Fair Graph Federated Learning via Incentive Mechanisms. [PUB] [PAGE] [PDF] [CODE]
• Towards the Robustness of Differentially Private Federated Learning. [PUB] [PAGE]
• Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective. [PUB] [PAGE] [PDF] [CODE]
• Integer Is Enough: When Vertical Federated Learning Meets Rounding. [PUB] [PAGE]
• CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data. [PUB] [PAGE] [PDF] [CODE]
• Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning. [PUB] [PAGE] [PDF] [CODE]
• Multi-Dimensional Fair Federated Learning. [PUB] [PAGE] [PDF]
• HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation. [PUB]
• On the Role of Server Momentum in Federated Learning. [PUB] [PDF]
• FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. [PUB] [PAGE] [PDF]
• z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning. [PUB] [PAGE] [PDF]
• Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets. [PUB] [PAGE]
• Federated Graph Learning under Domain Shift with Generalizable Prototypes. [PUB] [PAGE]
• TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. [PUB] [PAGE] [PDF] [CODE]
• Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization. [PUB] [PDF] [CODE]
• Concealing Sensitive Samples against Gradient Leakage in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. [PUB] [PAGE] [PDF] [CODE]
• Federated Causality Learning with Explainable Adaptive Optimization. [PUB] [PAGE] [PDF]
• Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users. [PUB] [PAGE] [PDF]
• Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models. [PUB] [PDF]
• Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification. [PUB] [PAGE]
• PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search. [PUB] [PAGE]
• Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction. [PUB] [PAGE]
• Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms. [PUB]
• FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• LR-XFL: Logical Reasoning-Based Explainable Federated Learning. [PUB] [PDF] [CODE]
• A Huber Loss Minimization Approach to Byzantine Robust Federated Learning. [PUB] [PAGE] [PDF]
• Knowledge-Aware Parameter Coaching for Personalized Federated Learning. [PUB] [PAGE]
• Federated Label-Noise Learning with Local Diversity Product Regularization. [PUB] [PAGE] [SUPP]
• Adapted Weighted Aggregation in Federated Learning (Student Abstract). [PUB]
• Knowledge Transfer via Compact Model in Federated Learning (Student Abstract). [PUB] [PAGE]
• PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract). [PUB] [PAGE]

2023

AI

• Privacy-preserving graph convolution network for federated item recommendation. [PUB]

AAAI

• Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation. [PUB]
• Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. [PUB] [PDF] [CODE]
• Incentive-Boosted Federated Crowdsourcing. [PUB] [PDF]
• Tackling Data Heterogeneity in Federated Learning with Class Prototypes. [PUB] [PDF] [CODE]
• FairFed: Enabling Group Fairness in Federated Learning. [PUB] [PDF] [解读]
• Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning. [PUB]
• Complement Sparsification: Low-Overhead Model Pruning for Federated Learning. [PUB]
• Almost Cost-Free Communication in Federated Best Arm Identification. [PUB] [PDF]
• Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning. [PUB] [PDF]
• Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning. [PUB]
• FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance. [PUB]
• Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. [PUB] [PDF] [VIDEO] [CODE]
• Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [PUB] [PDF] [CODE]
• Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. [PUB] [PDF] [CODE]
• FedABC: Targeting Fair Competition in Personalized Federated Learning. [PUB] [PDF]
• Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework. [PUB] [PDF]
• FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability. [PUB] [PDF] [CODE]
• Faster Adaptive Federated Learning. [PUB] [PDF]
• FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation. [PUB] [CODE] [VIDEO] [SUPP]
• Bayesian Federated Neural Matching That Completes Full Information. [PUB] [PDF]
• CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems. [PUB] [PDF] [CODE]
• Federated Generative Model on Multi-Source Heterogeneous Data in IoT. [PUB]
• DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. [PUB]
• FedALA: Adaptive Local Aggregation for Personalized Federated Learning. [PUB] [PDF] [CODE]
• Delving into the Adversarial Robustness of Federated Learning. [PUB] [PDF]
• On the Vulnerability of Backdoor Defenses for Federated Learning. [PUB] [PDF] [CODE]
• Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. [PUB] [PDF]
• Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [PDF] [CODE]
• FedGS: Federated Graph-based Sampling with Arbitrary Client Availability. [PDF] [CODE]
• Incentive-boosted Federated Crowdsourcing. [PDF]

AAAI Special Tracks

• DPAUC: Differentially Private AUC Computation in Federated Learning. [PUB] [PDF] [CODE]

AAAI Special Programs

• Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [PUB] [PDF]
• Industry-Scale Orchestrated Federated Learning for Drug Discovery. [PUB] [PDF] [VIDEO]
• A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [PUB]
• MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [PUB]
• Clustered Federated Learning for Heterogeneous Data (Student Abstract). [PUB]

IJCAI

• FedSampling: A Better Sampling Strategy for Federated Learning. [PUB] [PDF] [CODE]
• HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning. [PUB] [PDF]
• FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning. [PUB] [PDF] [CODE]
• Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation. [PUB]
• Federated Graph Semantic and Structural Learning. [PUB]
• BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning. [PUB] [PDF]
• FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment. [PUB] [PDF]
• FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation. [PUB] [PDF]
• Globally Consistent Federated Graph Autoencoder for Non-IID Graphs. [PUB] [CODE]
• Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [PUB]
• Dual Personalization on Federated Recommendation. [PUB] [PDF] [CODE]
• FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity. [PUB] [PDF] [CODE]
• Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning. [PUB] [PDF] [CODE]
• FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks. [PUB] [PDF] [CODE]
• FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer. [PUB] [PDF]
• Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. [PUB] [PDF] [CODE]
• FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training. [PUB] [CODE]

IJCAI Survey Track

• Bayesian Federated Learning: A Survey. [PDF]
• A Survey of Federated Evaluation in Federated Learning. [PUB] [PDF]

IJCAI Journal Track

• SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [PUB]

AISTATS

• The communication cost of security and privacy in federated frequency estimation. [PUB] [CODE]
• Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. [PUB] [CODE]
• Federated Learning under Distributed Concept Drift. [PUB] [CODE]
• Characterizing Internal Evasion Attacks in Federated Learning. [PUB] [CODE]
• Federated Asymptotics: a model to compare federated learning algorithms. [PUB] [CODE]
• Private Non-Convex Federated Learning Without a Trusted Server. [PUB] [CODE]
• Federated Learning for Data Streams. [PUB] [CODE]
• Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. [PUB] [CODE]
• Active Membership Inference Attack under Local Differential Privacy in Federated Learning. [PUB] [CODE]
• Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. [PUB]
• Byzantine-Robust Federated Learning with Optimal Statistical Rates. [PUB] [CODE]

2022

AISTATS

• Towards Understanding Biased Client Selection in Federated Learning. [PUB] [CODE]
• FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. [PUB] [PDF] [CODE]
• Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. [PUB] [PDF] [CODE]
• Federated Reinforcement Learning with Environment Heterogeneity. [PUB] [PDF] [CODE]
• Federated Myopic Community Detection with One-shot Communication. [PUB] [PDF]
• Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. [PUB] [PDF] [CODE]
• Towards Federated Bayesian Network Structure Learning with Continuous Optimization. [PUB] [PDF] [CODE]
• Federated Learning with Buffered Asynchronous Aggregation. [PUB] [PDF] [VIDEO]
• Differentially Private Federated Learning on Heterogeneous Data. [PUB] [PDF] [CODE]
• SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. [PUB] [PDF] [CODE] [VIDEO]
• Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. [PUB] [PDF]
• Federated Functional Gradient Boosting. [PUB] [PDF] [CODE]
• QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. [PUB] [PDF] [CODE] [VIDEO]

IJCAI

• Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg.. [PUB] [PDF] [CODE]
• Personalized Federated Learning With a Graph. [PUB] [PDF] [CODE]
• Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification. [PUB] [PDF]
• Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. [PUB] [PDF] [CODE]
• Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning. [PUB] [PDF]
• Private Semi-Supervised Federated Learning. [PUB]
• Continual Federated Learning Based on Knowledge Distillation. [PUB]
• Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features. [PUB] [PDF] [CODE]
• Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. [PUB]
• Personalized Federated Learning with Contextualized Generalization. [PUB] [PDF]
• Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. [PUB] [PDF]
• FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. [PUB] [PDF] [CODE]
• FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. [PUB] [PDF]
• Towards Verifiable Federated Learning surv.. [PUB] [PDF]

AAAI

• HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images. [PUB] [PDF] [CODE] [解读]
• Federated Learning for Face Recognition with Gradient Correction. [PUB] [PDF]
• SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data. [PUB] [PDF] [CODE] [解读]
• SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures. [PUB] [CODE]
• Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network. [PUB] [PDF]
• Seizing Critical Learning Periods in Federated Learning. [PUB] [PDF]
• Coordinating Momenta for Cross-silo Federated Learning. [PUB] [PDF]
• FedProto: Federated Prototype Learning over Heterogeneous Devices. [PUB] [PDF] [CODE]
• FedSoft: Soft Clustered Federated Learning with Proximal Local Updating. [PUB] [PDF] [CODE]
• Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. [PUB] [PDF] [CODE]
• FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition. [PUB] [PDF] [CODE]
• SplitFed: When Federated Learning Meets Split Learning. [PUB] [PDF] [CODE]
• Efficient Device Scheduling with Multi-Job Federated Learning. [PUB] [PDF]
• Implicit Gradient Alignment in Distributed and Federated Learning. [PUB] [PDF]
• Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. [PUB] [PDF] [CODE]

ALT

• Iterated Vector Fields and Conservatism, with Applications to Federated Learning. [PUB] [PDF]

2021

IJCAI

• Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization. [PUB] [PDF] [VIDEO]
• Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. [PUB] [PDF]
• FedSpeech: Federated Text-to-Speech with Continual Learning. [PUB] [PDF]
• Practical One-Shot Federated Learning for Cross-Silo Setting. [PUB] [PDF] [CODE]
• Federated Model Distillation with Noise-Free Differential Privacy. [PUB] [PDF] [VIDEO]
• LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. [PUB] [PDF]
• Federated Learning with Fair Averaging. . [PUB] [PDF] [CODE]
• H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. [PUB] [PDF]
• Communication-efficient and Scalable Decentralized Federated Edge Learning. [PUB]

AAAI

• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. [PUB] [PDF] [VIDEO]
• FedRec++: Lossless Federated Recommendation with Explicit Feedback. [PUB] [VIDEO]
• Federated Multi-Armed Bandits. [PUB] [PDF] [CODE] [VIDEO]
• On the Convergence of Communication-Efficient Local SGD for Federated Learning. [PUB] [VIDEO]
• FLAME: Differentially Private Federated Learning in the Shuffle Model. [PUB] [PDF] [VIDEO] [CODE]
• Toward Understanding the Influence of Individual Clients in Federated Learning. [PUB] [PDF] [VIDEO]
• Provably Secure Federated Learning against Malicious Clients. [PUB] [PDF] [VIDEO] [SLIDE]
• Personalized Cross-Silo Federated Learning on Non-IID Data. [PUB] [PDF] [VIDEO] [UC.]
• Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation. [PUB] [PDF] [CODE] [VIDEO]
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [PUB] [PDF] [VIDEO]
• Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models. [PUB] [PDF] [VIDEO] [CODE]
• Addressing Class Imbalance in Federated Learning. [PUB] [PDF] [VIDEO] [CODE] [解读]
• Defending against Backdoors in Federated Learning with Robust Learning Rate. [PUB] [PDF] [VIDEO] [CODE]

AISTATS

• Free-rider Attacks on Model Aggregation in Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Federated f-differential privacy. [PUB] [CODE] [VIDEO] [SUPP]
• Federated learning with compression: Unified analysis and sharp guarantees . [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Shuffled Model of Differential Privacy in Federated Learning. [PUB] [VIDEO] [SUPP]
• Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. [PUB] [PDF] [VIDEO] [SUPP]
• Federated Multi-armed Bandits with Personalization. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Towards Flexible Device Participation in Federated Learning. [PUB] [PDF] [VIDEO] [SUPP]

2020

IJCAI

• Federated Meta-Learning for Fraudulent Credit Card Detection. [PUB] [VIDEO]
• A Multi-player Game for Studying Federated Learning Incentive Schemes. [PUB] [CODE] [解读]

AAAI

• Practical Federated Gradient Boosting Decision Trees. [PUB] [PDF] [CODE]
• Federated Learning for Vision-and-Language Grounding Problems. [PUB]
• Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. [PUB]
• Federated Patient Hashing. [PUB]
• Robust Federated Learning via Collaborative Machine Teaching. [PUB] [PDF]
• FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. [PUB] [PDF] [CODE]

AISTATS

• FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. [PUB] [PDF] [VIDEO] [SUPP]
• How To Backdoor Federated Learning . [PUB] [PDF] [VIDEO] [CODE] [SUPP]
• Federated Heavy Hitters Discovery with Differential Privacy. [PUB] [PDF] [VIDEO] [SUPP]

2019

IJCAI

• Multi-Agent Visualization for Explaining Federated Learning. [PUB] [VIDEO]

fl in top ml conference and journal

Federated Learning papers accepted by top ML(machine learning) conference and journal, Including NeurIPS(Annual Conference on Neural Information Processing Systems), ICML(International Conference on Machine Learning), ICLR(International Conference on Learning Representations), COLT(Annual Conference Computational Learning Theory) , UAI(Conference on Uncertainty in Artificial Intelligence),Machine Learning, JMLR(Journal of Machine Learning Research), TPAMI(IEEE Transactions on Pattern Analysis and Machine Intelligence).

• NeurIPS 2024(OpenReview), 2023(OpenReview), 2022(OpenReview), 2021(OpenReview), 2020, 2018, 2017
• ICML 2025, 2024, 2023, 2022, 2021, 2020, 2019
• ICLR 2025, 2024, 2023, 2022, 2021, 2020
• COLT 2023
• UAI 2025, 2024, 2023, 2022, 2021
• Machine Learning 2025, 2024, 2023, 2022
• JMLR 2024(v25), 2023(v24), 2021(v22)
• TPAMI 2025, 2024, 2023, 2022

fl in top ml conference and journal

2025

UAI

• Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication. [PUB]
• FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning. [PUB]
• FeDCM: Federated Learning of Deep Causal Generative Models. [PUB]
• Federated Rényi Fair Inference in Federated Heterogeneous System. [PUB]
• FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning. [PUB]
• ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. [PUB]
• FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set. [PUB]
• Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning. [PUB]
• Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information. [PUB]

ICML

• Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off. [PUB] [CODE]
• Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective. [PUB]
• SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding. [PUB] [CODE]
• Causality Inspired Federated Learning for OOD Generalization. [PUB]
• Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. [PUB] [CODE]
• One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models. [PUB] [CODE]
• FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. [PUB]
• An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. [PUB] [CODE]
• Gap-Dependent Bounds for Federated $Q$-Learning. [PUB]
• FedBEns: One-Shot Federated Learning based on Bayesian Ensemble. [PUB] [CODE]
• NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel. [PUB] [CODE]
• Federated Learning for Feature Generalization with Convex Constraints. [PUB] [CODE]
• Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos. [PUB] [CODE]
• Towards Trustworthy Federated Learning with Untrusted Participants. [PUB]
• Multi-Session Budget Optimization for Forward Auction-based Federated Learning. [PUB]
• Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation. [PUB] [CODE]
• LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation. [PUB]
• Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning. [PUB]
• Differentially Private Federated $k$-Means Clustering with Server-Side Data. [PUB] [CODE]
• CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning. [PUB]
• Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees. [PUB] [CODE]
• $S^2$FGL: Spatial Spectral Federated Graph Learning. [PUB] [CODE]
• FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. [PUB] [CODE]
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning. [PUB] [CODE]
• Efficient Heterogeneity-Aware Federated Active Data Selection. [PUB]
• Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models. [PUB] [CODE]
• Rethinking the Temperature for Federated Heterogeneous Distillation. [PUB]
• FedClean: A General Robust Label Noise Correction for Federated Learning. [PUB]
• Federated Causal Structure Learning with Non-identical Variable Sets. [PUB]
• FedECADO: A Dynamical System Model of Federated Learning. [PUB]
• Efficient Federated Incomplete Multi-View Clustering. [PUB] [CODE]
• Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance. [PUB] [CODE]
• Local Pan-privacy for Federated Analytics. [PUB]
• FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. [PUB] [CODE]
• Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning. [PUB] [CODE]
• Private Federated Learning using Preference-Optimized Synthetic Data. [PUB] [CODE]
• Enhancing Foundation Models with Federated Domain Knowledge Infusion. [PUB]
• FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation. [PUB] [CODE]
• Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework. [PUB] [CODE]
• Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. [PUB]
• Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [PUB] [CODE]
• FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence. [PUB]
• Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework. [PUB] [CODE]
• DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence. [PUB]
• On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists. [PUB]
• Safe-EF: Error Feedback for Non-smooth Constrained Optimization. [PUB] [CODE]
• Gradient Inversion of Multimodal Models. [PUB] [CODE]
• Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg. [PUB] [CODE]
• Decoupled SGDA for Games with Intermittent Strategy Communication. [PUB]
• Private Model Personalization Revisited. [PUB]
• Leveraging Randomness in Model and Data Partitioning for Privacy Amplification. [PUB]
• Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. [PUB] [CODE]
• Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning. [PUB] [CODE]
• FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates. [PUB] [CODE]
• One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory. [PUB] [CODE]
• Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. [PUB]
• Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning. [PUB] [CODE]
• GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention. [PUB] [CODE]
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing. [PUB]
• BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach. [PUB]
• Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning. [PUB]
• EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification. [PUB] [CODE]
• Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference. [PUB] [CODE]
• Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models. [PUB] [CODE]
• Generalization in Federated Learning: A Conditional Mutual Information Framework. [PUB]
• The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning. [PUB] [CODE]
• Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions. [PUB] [CODE]
• Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. [PUB]
• Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. [PUB]
• SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution. [PUB] [CODE]
• You Get What You Give: Reciprocally Fair Federated Learning. [PUB]
• Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead. [PUB] [CODE]
• Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning. [PUB]

Mach Learn

• HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [PUB]
• Fedflow: a personalized federated learning framework for passenger flow prediction. [PUB]
• Federated causal inference from observational data. [PUB]
• TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [PUB]
• Improve global generalization for personalized federated learning within a Stackelberg game. [PUB]
• Efficient federated unlearning under plausible deniability. [PUB] [CODE]

ICLR

• Energy-based Backdoor Defense Against Federated Graph Learning. [PUB]
• DEPT: Decoupled Embeddings for Pre-training Language Models. [PUB]
• Subgraph Federated Learning for Local Generalization. [PUB] [CODE]
• Problem-Parameter-Free Federated Learning. [PUB]
• Adaptive Gradient Clipping for Robust Federated Learning. [PUB]
• Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees. [PUB]
• LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. [PUB]
• Group Distributionally Robust Dataset Distillation with Risk Minimization. [PUB]
• GRAIN: Exact Graph Reconstruction from Gradients. [PUB]
• Towards Faster Decentralized Stochastic Optimization with Communication Compression. [PUB]
• Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. [PUB]
• Many-Objective Multi-Solution Transport. [PUB]
• Query-based Knowledge Transfer for Heterogeneous Learning Environments. [PUB]
• Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting. [PUB]
• Federated Granger Causality Learning For Interdependent Clients With State Space Representation. [PUB]
• Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization. [PUB]
• Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. [PUB]
• On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning. [PUB]
• PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models. [PUB]
• Differentially Private Federated Learning with Time-Adaptive Privacy Spending. [PUB]
• Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies. [PUB]
• Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis. [PUB]
• On the Byzantine-Resilience of Distillation-Based Federated Learning. [PUB]
• Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [PUB]
• Event-Driven Online Vertical Federated Learning. [PUB]
• On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations. [PUB]
• Federated Domain Generalization with Data-free On-server Matching Gradient. [PUB]
• Unlocking the Potential of Model Calibration in Federated Learning. [PUB]
• FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking. [PUB]
• Understanding the Stability-based Generalization of Personalized Federated Learning. [PUB]
• Federated Residual Low-Rank Adaption of Large Language Models. [PUB]
• FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine. [PUB]
• Vertical Federated Learning with Missing Features During Training and Inference. [PUB]
• Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost. [PUB]
• Selective Aggregation for Low-Rank Adaptation in Federated Learning. [PUB]
• Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models. [PUB]
• Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. [PUB]
• Debiasing Federated Learning with Correlated Client Participation. [PUB]
• Decoupled Subgraph Federated Learning. [PUB]
• Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning. [PUB]
• Towards Federated RLHF with Aggregated Client Preference for LLMs. [PUB]
• SparsyFed: Sparse Adaptive Federated Learning. [PUB]
• Can Textual Gradient Work in Federated Learning?. [PUB]
• Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models. [PUB]
• Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning. [PUB]
• Connecting Federated ADMM to Bayes. [PUB]
• Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning. [PUB]
• Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond. [PUB]
• Federated Few-Shot Class-Incremental Learning. [PUB]

TPAMI

• Re-Fed+: A Better Replay Strategy for Federated Incremental Learning. [PUB]
• DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning. [PUB]
• Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients. [PUB]
• Federated Multi-View K-Means Clustering. [PUB]
• Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation. [PUB]
• Medical Federated Model With Mixture of Personalized and Shared Components. [PUB]

2024

UAI

• FedAST: Federated Asynchronous Simultaneous Training. [PUB]
• On Convergence of Federated Averaging Langevin Dynamics. [PUB]
• On the Convergence of Hierarchical Federated Learning with Partial Worker Participation. [PUB]
• Pure Exploration in Asynchronous Federated Bandits. [PUB]

NeurIPS

• One-shot Federated Learning via Synthetic Distiller-Distillate Communication. [PUB]
• Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. [PUB]
• FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation. [PUB]
• Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach. [PUB]
• Federated Model Heterogeneous Matryoshka Representation Learning. [PUB]
• Federated Graph Learning for Cross-Domain Recommendation. [PUB]
• FedGMark: Certifiably Robust Watermarking for Federated Graph Learning. [PUB]
• Dual-Personalizing Adapter for Federated Foundation Models. [PUB]
• Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. [PUB]
• Taming the Long Tail in Human Mobility Prediction. [PUB]
• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning. [PUB]
• Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution. [PUB]
• DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting. [PUB]
• Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability. [PUB]
• Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. [PUB]
• FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction. [PUB]
• Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. [PUB]
• FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. [PUB]
• Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains. [PUB]
• pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning. [PUB]
• Why Go Full? Elevating Federated Learning Through Partial Network Updates. [PUB]
• FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. [PUB]
• FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference. [PUB]
• Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. [PUB]
• A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. [PUB]
• Private and Personalized Frequency Estimation in a Federated Setting. [PUB]
• The Sample-Communication Complexity Trade-off in Federated Q-Learning. [PUB]
• Federated Ensemble-Directed Offline Reinforcement Learning. [PUB]
• Federated Black-Box Adaptation for Semantic Segmentation. [PUB]
• Thinking Forward: Memory-Efficient Federated Finetuning of Language Models. [PUB]
• Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. [PUB]
• Optimal Design for Human Preference Elicitation. [PUB]
• Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. [PUB]
• Personalized Federated Learning via Feature Distribution Adaptation. [PUB]
• SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. [PUB]
• A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. [PUB]
• RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation. [PUB]
• FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. [PUB]
• End-to-end Learnable Clustering for Intent Learning in Recommendation. [PUB]
• FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation. [PUB]
• Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting. [PUB]
• FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection. [PUB]
• A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm. [PUB]
• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. [PUB]
• Low Precision Local Training is Enough for Federated Learning. [PUB]
• Resource-Aware Federated Self-Supervised Learning with Global Class Representations. [PUB]
• On the Necessity of Collaboration for Online Model Selection with Decentralized Data. [PUB]
• The Power of Extrapolation in Federated Learning. [PUB]
• (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning. [PUB]
• On Sampling Strategies for Spectral Model Sharding. [PUB]
• Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. [PUB]
• SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. [PUB]
• HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning. [PUB]
• Stabilized Proximal-Point Methods for Federated Optimization. [PUB]
• DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices. [PUB]
• Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning. [PUB]
• Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD. [PUB]
• FedAvP: Augment Local Data via Shared Policy in Federated Learning. [PUB]
• CoBo: Collaborative Learning via Bilevel Optimization. [PUB]
• Convergence Analysis of Split Federated Learning on Heterogeneous Data. [PUB]
• Communication-Efficient Federated Group Distributionally Robust Optimization. [PUB]
• Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity. [PUB]
• Federated Learning over Connected Modes. [PUB]
• Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning. [PUB]
• Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors. [PUB]
• Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups. [PUB]
• DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans. [PUB]
• Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. [PUB]
• Hierarchical Federated Learning with Multi-Timescale Gradient Correction. [PUB]
• HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links. [PUB]
• SPEAR: Exact Gradient Inversion of Batches in Federated Learning. [PUB]
• Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. [PUB]
• Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views. [PUB]
• Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data. [PUB]
• Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning. [PUB]
• Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. [PUB]
• Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. [PUB]
• Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning. [PUB]
• FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?. [PUB]
• Active preference learning for ordering items in- and out-of-sample. [PUB]
• Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources. [PUB]
• Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. [PUB]
• Revisiting Ensembling in One-Shot Federated Learning. [PUB]
• FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models. [PUB]
• $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [PUB]
• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection. [PUB]

NeurIPS workshop

• Momentum Approximation in Asynchronous Private Federated Learning. [PUB]
• Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. [PUB]
• Federated Learning with Generative Content. [PUB]
• Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models. [PUB]
• Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [PUB]
• Defection-Free Collaboration between Competitors in a Learning System. [PUB]
• On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments. [PUB]
• EncCluster: Bringing Functional Encryption in Federated Foundational Models. [PUB]
• Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [PUB]
• Hot Pluggable Federated Learning. [PUB]
• Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees. [PUB]
• The Future of Large Language Model Pre-training is Federated. [PUB]
• Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms. [PUB]
• The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain. [PUB]
• ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning. [PUB]
• DeComFL: Federated Learning with Dimension-Free Communication. [PUB]
• Improving Group Connectivity for Generalization of Federated Deep Learning. [PUB]
• MAP: Model Merging with Amortized Pareto Front Using Limited Computation. [PUB]
• OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning. [PUB]
• Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration. [PUB]
• Worldwide Federated Training of Language Models. [PUB]
• FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator. [PUB]
• Enhancing Causal Discovery in Federated Settings with Limited Local Samples. [PUB]
• $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [PUB]
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing. [PUB]

JMLR

• FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. [PUB]

ICML

• Effective Federated Graph Matching. [PUB]
• Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. [PUB]
• Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients. [PUB]
• FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models. [PUB]
• Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. [PUB]
• A New Theoretical Perspective on Data Heterogeneity in Federated Optimization. [PUB]
• Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models. []
• Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments. [PUB]
• Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. [PUB]
• Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. [PUB]
• Accelerating Federated Learning with Quick Distributed Mean Estimation. [PUB]
• Fair Federated Learning via the Proportional Veto Core. [PUB]
• AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning. [PUB]
• Recovering Labels from Local Updates in Federated Learning. [PUB]
• FedMBridge: Bridgeable Multimodal Federated Learning. [PUB]
• Harmonizing Generalization and Personalization in Federated Prompt Learning. [PUB]
• Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. [PUB]
• Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. [PUB]
• Federated Combinatorial Multi-Agent Multi-Armed Bandits. [PUB]
• A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. [PUB]
• Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. [PUB]
• FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering. [PUB]
• Pursuing Overall Welfare in Federated Learning through Sequential Decision Making. [PUB]
• PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. [PUB]
• Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning. [PUB]
• Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors. [PUB]
• Federated Optimization with Doubly Regularized Drift Correction. [PUB]
• FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data. [PUB]
• Certifiably Byzantine-Robust Federated Conformal Prediction. [PUB]
• Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning. [PUB]
• Clustered Federated Learning via Gradient-based Partitioning. [PUB]
• Recurrent Early Exits for Federated Learning with Heterogeneous Clients. [PUB]
• Rethinking the Flat Minima Searching in Federated Learning. [PUB]
• FedBAT: Communication-Efficient Federated Learning via Learnable Binarization. [PUB]
• Federated Representation Learning in the Under-Parameterized Regime. [PUB]
• FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. [PUB]
• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. [PUB]
• SILVER: Single-loop variance reduction and application to federated learning. [PUB]
• SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. [PUB]
• FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler. [PUB]
• Federated Continual Learning via Prompt-based Dual Knowledge Transfer. [PUB]
• Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes. [PUB]
• Decomposable Submodular Maximization in Federated Setting. [PUB]
• Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. [PUB]
• Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials. [PUB]
• Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!. [PUB]
• Byzantine Resilient and Fast Federated Few-Shot Learning. [PUB]
• Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization. [PUB]
• Ranking-based Client Imitation Selection for Efficient Federated Learning. [PUB]
• Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. [PUB]
• FADAS: Towards Federated Adaptive Asynchronous Optimization. [PUB]
• Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices. [PUB]
• FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error. [PUB]
• MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis. [PUB]
• Federated Neuro-Symbolic Learning. [PUB]
• Adaptive Group Personalization for Federated Mutual Transfer Learning. [PUB]
• Balancing Similarity and Complementarity for Federated Learning. [PUB]
• Federated Self-Explaining GNNs with Anti-shortcut Augmentations. [PUB]
• A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. [PUB]
• COALA: A Practical and Vision-Centric Federated Learning Platform. [PUB]

Mach Learn

• Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [PUB]
• Communication-efficient clustered federated learning via model distance. [PUB]
• Federated learning with superquantile aggregation for heterogeneous data. [PUB] [PDF] [CODE]
• Aligning model outputs for class imbalanced non-IID federated learning. [PUB]

TPAMI

• Federated Learning of Generalized Linear Causal Networks. [PUB]
• Cross-Modal Federated Human Activity Recognition. [PUB]
• Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling. [PUB] [PDF] [CODE]
• The Impact of Adversarial Attacks on Federated Learning: A Survey. [PUB]
• Understanding and Mitigating Dimensional Collapse in Federated Learning. [PUB] [PDF] [CODE]
• No One Left Behind: Real-World Federated Class-Incremental Learning. [PUB] [PDF] [CODE]
• Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning. [PUB] [PDF] [CODE]
• Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy. [PUB] [PDF] [CODE]
• A Bayesian Federated Learning Framework With Online Laplace Approximation. [PUB] [PDF] [CODE]

ICLR

• Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. [PUB]
• One-shot Empirical Privacy Estimation for Federated Learning. [PUB] [PDF]
• Stochastic Controlled Averaging for Federated Learning with Communication Compression. [PUB] [PDF]
• A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. [PUB] [PDF] [CODE]
• A Mutual Information Perspective on Federated Contrastive Learning. [PUB]
• Benchmarking Algorithms for Federated Domain Generalization. [PUB] [PDF] [CODE]
• Effective and Efficient Federated Tree Learning on Hybrid Data. [PUB] [PDF]
• Federated Recommendation with Additive Personalization. [PUB] [PDF] [CODE]
• Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration. [PUB] [SUPP]
• Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning. [PUB] [SUPP] [PDF]
• Accurate Forgetting for Heterogeneous Federated Continual Learning. [PUB] [CODE]
• Federated Causal Discovery from Heterogeneous Data. [PUB] [PDF] [CODE]
• On Differentially Private Federated Linear Contextual Bandits. [PUB] [SUPP] [PDF]
• Incentivized Truthful Communication for Federated Bandits. [PUB] [PDF]
• Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting. [PUB]
• FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. [PUB]
• Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [PUB] [PDF]
• Improving LoRA in Privacy-preserving Federated Learning. [PUB]
• FedWon: Triumphing Multi-domain Federated Learning Without Normalization. [PUB] [PDF]
• FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning. [PUB]
• FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler. [PUB] [PDF] [CODE] [PAGE]
• Bayesian Coreset Optimization for Personalized Federated Learning. [PUB]
• Layer-wise linear mode connectivity. [PUB] [PDF] [SUPP]
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation. [PUB] [PDF]
• Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. [PUB] [SUPP] [PDF]
• Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning. [PUB] [PDF]
• Adaptive Federated Learning with Auto-Tuned Clients. [PUB] [SUPP] [PDF]
• Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. [PUB] [SUPP] [PDF]
• Federated Q-Learning: Linear Regret Speedup with Low Communication Cost. [PUB] [SUPP] [PDF]
• FedImpro: Measuring and Improving Client Update in Federated Learning. [PUB] [PDF]
• Federated Wasserstein Distance. [PUB] [SUPP] [PDF]
• An improved analysis of per-sample and per-update clipping in federated learning. [PUB]
• FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation. [PUB] [SUPP]
• Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning. [PUB] [PDF]
• Momentum Benefits Non-iid Federated Learning Simply and Provably. [PUB] [PDF]
• Communication-Efficient Federated Non-Linear Bandit Optimization. [PUB] [PDF]
• Fair and Efficient Contribution Valuation for Vertical Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [PUB] [PDF]
• Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients. [PUB]
• PeFLL: Personalized Federated Learning by Learning to Learn. [PUB] [SUPP] [PDF]
• Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. [PUB] [SUPP] [PDF]
• FedInverse: Evaluating Privacy Leakage in Federated Learning. [PUB] [SUPP]
• FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. [PUB] [SUPP] [PDF]
• Robust Training of Federated Models with Extremely Label Deficiency. [PUB] [PDF] [CODE]
• Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. [PUB]
• Teach LLMs to Phish: Stealing Private Information from Language Models. [PUB]
• Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix. [PUB]
• Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. [PUB] [PDF]
• Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks. [PUB] [SUPP] [PDF] [CODE]
• Local Composite Saddle Point Optimization. [PUB] [PDF]
• Enhancing Neural Training via a Correlated Dynamics Model. [PUB] [PDF]
• EControl: Fast Distributed Optimization with Compression and Error Control. [PUB] [SUPP] [PDF]
• Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit. [PUB]
• FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent. [PUB] [SUPP] [PDF] [CODE]
• Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate. [PUB]
• Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [PUB]
• Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity. [PUB]
• VFLAIR: A Research Library and Benchmark for Vertical Federated Learning. [PUB] [PDF] [CODE]
• Incentive-Aware Federated Learning with Training-Time Model Rewards. [PUB] [SUPP]
• VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. [PUB] [PDF] [CODE]
• FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data. [PUB] [SUPP] [PDF]

2023

NeurIPS

• SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. [PUB] [PDF] [SUPP]
• Mechanism Design for Collaborative Normal Mean Estimation. [PUB] [PDF]
• Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. [PUB] [PDF] [CODE]
• Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. [PUB] [SUPP]
• Convergence Analysis of Sequential Federated Learning on Heterogeneous Data. [PUB] [PDF] [CODE]
• Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. [PUB] [PDF] [CODE]
• Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. [PUB] [SUPP] [PDF]
• Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. [PUB] [SUPP] [PDF]
• Incentivized Communication for Federated Bandits. [PUB] [PDF]
• Multiply Robust Federated Estimation of Targeted Average Treatment Effects. [PUB] [PDF]
• IBA: Towards Irreversible Backdoor Attacks in Federated Learning. [PUB] [SUPP] [CODE]
• EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning. [PUB] [SUPP] [PDF]
• Federated Linear Bandits with Finite Adversarial Actions. [PUB] [SUPP] [PDF]
• FedNAR: Federated Optimization with Normalized Annealing Regularization. [PUB] [SUPP] [PDF] [CODE]
• Guiding The Last Layer in Federated Learning with Pre-Trained Models. [PUB] [SUPP] [PDF] [CODE]
• Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. [PUB] [SUPP]
• Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. [PUB] [SUPP] [PDF] [CODE]
• A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. [PUB] [PDF] [CODE]
• Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning. [PUB] [SUPP]
• One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. [PUB] [SUPP] [CODE]
• Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. [PUB] [SUPP] [CODE]
• FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. [PUB] [SUPP] [CODE]
• Towards Personalized Federated Learning via Heterogeneous Model Reassembly. [PUB] [SUPP] [PDF] [CODE]
• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction. [PUB] [SUPP] [PDF]
• DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. [PUB] [SUPP] [CODE]
• RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks. [PUB] [SUPP] [PDF]
• Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. [PUB] [SUPP] [CODE]
• Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. [PUB] [SUPP] [CODE]
• FedL2P: Federated Learning to Personalize. [PUB] [SUPP] [PDF] [CODE]
• Adaptive Test-Time Personalization for Federated Learning. [PUB] [PDF] [CODE]
• Federated Conditional Stochastic Optimization. [PUB] [SUPP] [PDF] [CODE]
• Federated Spectral Clustering via Secure Similarity Reconstruction. [PUB]
• Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM. [PUB] [SUPP] [PDF]
• FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks. [PUB] [SUPP] [PDF] [CODE]
• Federated Multi-Objective Learning. [PUB] [SUPP] [PDF]
• FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout. [PUB] [SUPP] [PDF] [CODE]
• Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. [PUB] [SUPP]
• Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. [PUB] [SUPP] [PDF]
• StableFDG: Style and Attention Based Learning for Federated Domain Generalization. [PUB] [PDF]
• Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization. [PUB] [SUPP] [PDF]
• DELTA: Diverse Client Sampling for Fasting Federated Learning. [PUB] [SUPP] [PDF]
• Federated Compositional Deep AUC Maximization. [PUB] [SUPP] [PDF]
• A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning. [PUB] [SUPP] [CODE]
• Flow: Per-instance Personalized Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Eliminating Domain Bias for Federated Learning in Representation Space. [PUB] [SUPP] [PDF] [CODE]
• Federated Learning with Manifold Regularization and Normalized Update Reaggregation. [PUB] [SUPP] [PDF]
• Structured Federated Learning through Clustered Additive Modeling. [PUB] [SUPP]
• Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer. [PUB] [SUPP] [PDF] [CODE]
• Dynamic Personalized Federated Learning with Adaptive Differential Privacy. [PUB] [SUPP] [CODE]
• Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Solving a Class of Non-Convex Minimax Optimization in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Federated Learning via Meta-Variational Dropout. [PUB] [CODE]
• Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. [PUB] [SUPP] [PDF]
• SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning. [PUB] [CODE]
• Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense. [PUB] [SUPP]
• FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. [PUB] [PDF] [CODE]
• PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. [PUB] [SUPP] [PDF] [CODE] [解读]
• Spectral Co-Distillation for Personalized Federated Learning. [PUB]
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy. [PUB] [SUPP] [PDF]
• Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation. [PUB] [SUPP] [PDF] [CODE]
• (Amplified) Banded Matrix Factorization: A unified approach to private training. [PUB] [SUPP] [PDF]
• Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices. [PUB] [SUPP] [PDF] [CODE]
• Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. [PUB] [SUPP] [PDF]
• Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. [PUB] [SUPP] [PDF]
• Resilient Constrained Learning. [PUB] [SUPP] [PDF]
• A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. [PUB] [SUPP] [PDF] [CODE]
• Collaboratively Learning Linear Models with Structured Missing Data. [PUB] [SUPP] [PDF] [CODE]
• Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. [PUB] [SUPP] [PDF]
• Fast Optimal Locally Private Mean Estimation via Random Projections. [PUB] [SUPP] [PDF] [CODE]
• Contextual Stochastic Bilevel Optimization. [PUB] [SUPP] [PDF]
• Understanding Deep Gradient Leakage via Inversion Influence Functions. [PUB] [SUPP] [PDF] [CODE]
• Inner Product-based Neural Network Similarity. [PUB] [SUPP]
• Correlation Aware Sparsified Mean Estimation Using Random Projection. [PUB] [SUPP] [PDF] [CODE]
• TIES-Merging: Resolving Interference When Merging Models. [PUB] [SUPP] [PDF] [CODE]
• Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data. [PUB] [SUPP] [PDF] [CODE]
• Large-Scale Distributed Learning via Private On-Device LSH. [PUB] [SUPP] [PDF]
• Faster Relative Entropy Coding with Greedy Rejection Coding. [PUB] [SUPP] [PDF] [CODE]
• Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. [PUB] [SUPP]
• Momentum Provably Improves Error Feedback!. [PUB] [SUPP] [PDF]
• Strategic Data Sharing between Competitors. [PUB] [SUPP] [PDF]
• H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets. [PUB]

NeurIPS Datasets and Benchmarks

• Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [PUB] [SUPP] [DATASET]
• Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [PUB] [PDF] [DATASET]

NeurIPS workshop

• Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [PUB]
• HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning. [PUB]
• Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning. [PUB]
• FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. [PUB]
• FedSoL: Bridging Global Alignment and Local Generality in Federated Learning. [PUB]
• One-shot Empirical Privacy Estimation for Federated Learning. [PUB]
• Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning. [PUB]
• SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models. [PUB]
• The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. [PUB]
• Towards Building the FederatedGPT: Federated Instruction Tuning. [PUB]
• Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR. [PUB]
• LASER: Linear Compression in Wireless Distributed Optimization. [PUB]
• MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization. [PUB]
• TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation. [PUB]
• An Empirical Evaluation of Federated Contextual Bandit Algorithms. [PUB]
• RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. [PUB]
• FDAPT: Federated Domain-adaptive Pre-training for Language Models. [PUB]
• Making Batch Normalization Great in Federated Deep Learning. [PUB]
• Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. [PUB]
• Parameter Averaging Laws for Multitask Language Models. [PUB]
• Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [PUB]
• Beyond Parameter Averaging in Model Aggregation. [PUB]
• Augmenting Federated Learning with Pretrained Transformers. [PUB]
• Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization. [PUB]
• DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. [PUB]
• Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning. [PUB]
• FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System. [PUB]
• Learning Optimizers for Local SGD. [PUB]
• Exploring User-level Gradient Inversion with a Diffusion Prior. [PUB]
• User Inference Attacks on Large Language Models. [PUB]
• FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis. [PUB]
• Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. [PUB]
• Backdoor Threats from Compromised Foundation Models to Federated Learning. [PUB]
• MOFL/D: A Federated Multi-objective Learning Framework with Decomposition. [PUB]
• Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning. [PUB]
• Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models. [PUB]
• FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning. [PUB]
• Private and Personalized Histogram Estimation in a Federated Setting. [PUB]

COLT

• The Aggregation–Heterogeneity Trade-off in Federated Learning. [PUB]

UAI

• FLASH: Automating federated learning using CASH. [PUB] [SUPP] [MATERIAL]
• Personalized federated domain adaptation for item-to-item recommendation. [PUB] [PDF] [SUPP] [MATERIAL] [CODE]
• Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. [PUB] [PDF] [SUPP] [MATERIAL]
• Federated learning of models pre-trained on different features with consensus graphs. [PUB] [SUPP] [MATERIAL] [CODE]
• Fast Heterogeneous Federated Learning with Hybrid Client Selection. [PUB] [SUPP] [MATERIAL] [PDF]
• Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. [PUB] [PDF] [SUPP] [MATERIAL] [CODE]

ICML

• Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. [PUB] [PDF] [SLIDES]
• Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. [PUB] [PDF]
• FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. [PUB] [PDF] [CODE]
• Federated Conformal Predictors for Distributed Uncertainty Quantification. [PUB] [PDF] [CODE]
• Federated Adversarial Learning: A Framework with Convergence Analysis. [PUB] [PDF]
• Federated Heavy Hitter Recovery under Linear Sketching. [PUB] [PDF] [CODE]
• Doubly Adversarial Federated Bandits. [PUB] [PDF] [CODE]
• Achieving Linear Speedup in Non-IID Federated Bilevel Learning. [PUB] [PDF]
• One-Shot Federated Conformal Prediction. [PUB] [PDF] [CODE]
• Federated Online and Bandit Convex Optimization. [PUB]
• Federated Linear Contextual Bandits with User-level Differential Privacy. [PUB] [PDF]
• Vertical Federated Graph Neural Network for Recommender System. [PUB] [PDF] [CODE]
• Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. [PUB] [PDF]
• Towards Understanding Ensemble Distillation in Federated Learning. [PUB]
• Personalized Subgraph Federated Learning. [PUB] [PDF] [CODE]
• Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. [PUB] [PDF]
• Secure Federated Correlation Test and Entropy Estimation. [PUB] [PDF]
• Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. [PUB] [CODE]
• Personalized Federated Learning under Mixture of Distributions. [PUB] [PDF] [CODE]
• FedDisco: Federated Learning with Discrepancy-Aware Collaboration. [PUB] [PDF] [CODE]
• Anchor Sampling for Federated Learning with Partial Client Participation. [PUB] [PDF] [CODE]
• Private Federated Learning with Autotuned Compression. [PUB] [PDF]
• Fast Federated Machine Unlearning with Nonlinear Functional Theory. [PUB]
• On the Convergence of Federated Averaging with Cyclic Client Participation. [PUB] [PDF]
• Revisiting Weighted Aggregation in Federated Learning with Neural Networks. [PUB] [PDF] [CODE]
• The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. [PUB] [PDF] [SLIDES]
• GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. [PUB]
• Flash: Concept Drift Adaptation in Federated Learning. [PUB]
• DoCoFL: Downlink Compression for Cross-Device Federated Learning. [PUB] [PDF]
• FeDXL: Provable Federated Learning for Deep X-Risk Optimization. [PUB] [PDF] [CODE]
• No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. [PUB] [CODE]
• Personalized Federated Learning with Inferred Collaboration Graphs. [PUB] [CODE]
• Optimizing the Collaboration Structure in Cross-Silo Federated Learning. [PUB] [PDF] [CODE] [SLIDES]
• TabLeak: Tabular Data Leakage in Federated Learning. [PUB] [PDF] [CODE]
• FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. [PUB] [CODE]
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. [PUB] [PDF]
• Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. [PUB] [PDF] [CODE]
• SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. [PUB] [PDF] [CODE]
• Improving the Model Consistency of Decentralized Federated Learning. [PUB] [PDF]
• Efficient Personalized Federated Learning via Sparse Model-Adaptation. [PUB] [PDF] [CODE]
• From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. [PUB] [PDF] [CODE]
• LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. [PUB] [CODE]
• Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. [PUB] [PDF] [CODE]
• FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. [PUB] [PDF]
• FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. [PUB] [PDF] [CODE]
• Towards Unbiased Training in Federated Open-world Semi-supervised Learning. [PUB] [PDF] [SLIDES]
• Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. [PUB] [PDF]
• Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. [PUB] [PDF] [CODE]
• Fair yet Asymptotically Equal Collaborative Learning. [PUB] [PDF] [CODE]
• Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. [PUB] [PDF]
• Adversarial Collaborative Learning on Non-IID Features. [PUB]
• XTab: Cross-table Pretraining for Tabular Transformers. [PUB] [PDF] [CODE]
• Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. [PUB]
• Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. [PUB] [PDF] [CODE]
• LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. [PUB] [PDF]
• FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. [PUB]
• Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. [PUB] [PDF] [CODE]

Mach Learn

• Ensemble and continual federated learning for classification tasks. [PUB] [PDF]
• FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [PUB]
• Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [PUB]

JMLR

• FedLab: A Flexible Federated Learning Framework . [PUB] [PDF] [CODE]
• Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?. [PUB]
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. [PUB] [PDF] [CODE]
• A First Look into the Carbon Footprint of Federated Learning. [PUB] [PDF]
• Attacks against Federated Learning Defense Systems and their Mitigation. [PUB] [CODE]
• A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. [PUB] [PDF] [CODE]

TPAMI

• Tighter Regret Analysis and Optimization of Online Federated Learning. [PUB] [PDF]
• Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup. [PDF]
• Federated Learning Via Inexact ADMM. [PUB] [PDF] [CODE]
• FedIPR: Ownership Verification for Federated Deep Neural Network Models. [PUB] [PDF] [CODE] [解读]
• Decentralized Federated Averaging. [PUB] [PDF]

ICLR

• Personalized Federated Learning with Feature Alignment and Classifier Collaboration. [PUB] [CODE]
• MocoSFL: enabling cross-client collaborative self-supervised learning. [PUB] [CODE]
• Single-shot General Hyper-parameter Optimization for Federated Learning. [PUB] [PDF] [CODE]
• Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated. [PUB] [PDF] [CODE]
• FedExP: Speeding up Federated Averaging via Extrapolation. [PUB] [PDF] [CODE]
• Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. [PUB] [CODE]
• DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. [PUB] [PDF] [CODE]
• Machine Unlearning of Federated Clusters. [PUB] [PDF] [CODE]
• Federated Neural Bandits. [PUB] [PDF] [CODE]
• FedFA: Federated Feature Augmentation. [PUB] [PDF] [CODE]
• Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. [PUB] [PDF] [CODE]
• Better Generative Replay for Continual Federated Learning. [PUB] [CODE]
• Federated Learning from Small Datasets. [PUB] [PDF]
• Federated Nearest Neighbor Machine Translation. [PUB] [PDF]
• Meta Knowledge Condensation for Federated Learning. [PUB] [PDF]
• Test-Time Robust Personalization for Federated Learning. [PUB] [PDF] [CODE]
• DepthFL : Depthwise Federated Learning for Heterogeneous Clients. [PUB]
• Towards Addressing Label Skews in One-Shot Federated Learning. [PUB] [CODE]
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. [PUB] [PDF] [CODE]
• Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. [PUB] [CODE]
• SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. [PUB] [PDF] [CODE]
• Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. [PUB] [PDF] [CODE]
• Effective passive membership inference attacks in federated learning against overparameterized models. [PUB]
• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. [PUB] [PDF] [CODE]
• Multimodal Federated Learning via Contrastive Representation Ensemble. [PUB] [PDF] [CODE]
• Faster federated optimization under second-order similarity. [PUB] [PDF] [CODE]
• FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. [PUB] [CODE]
• The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. [PUB] [PDF] [CODE]
• PerFedMask: Personalized Federated Learning with Optimized Masking Vectors. [PUB] [CODE]
• EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. [PUB] [CODE]
• FedDAR: Federated Domain-Aware Representation Learning. [PUB] [PDF] [CODE]
• Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. [PUB] [CODE]
• FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. [PUB] [PDF] [CODE]
• Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. [PUB]
• Efficient Federated Domain Translation. [PUB] [CODE]
• On the Importance and Applicability of Pre-Training for Federated Learning. [PUB] [PDF] [CODE]
• Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. [PUB] [PDF] [CODE]
• A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. [PUB] [PDF]
• Instance-wise Batch Label Restoration via Gradients in Federated Learning. [PUB] [CODE]
• Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. [PUB]
• CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. [PUB] [PDF] [CODE]
• Sparse Random Networks for Communication-Efficient Federated Learning. [PUB] [PDF] [CODE]
• Combating Exacerbated Heterogeneity for Robust Decentralized Models. [PUB] [CODE]
• Hyperparameter Optimization through Neural Network Partitioning. [PUB] [PDF]
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?. [PUB] [PDF] [CODE]
• Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. [PUB] [PDF] [CODE]
• Dual Diffusion Implicit Bridges for Image-to-Image Translation. [PUB] [PDF] [CODE]

2022

Mach Learn

• An accurate, scalable and verifiable protocol for federated differentially private averaging. [PUB] [PDF]

UAI

• Federated online clustering of bandits. [PUB] [PDF] [CODE]
• Privacy-aware compression for federated data analysis. [PUB] [PDF] [CODE]
• Faster non-convex federated learning via global and local momentum. [PUB] [PDF]
• Fedvarp: Tackling the variance due to partial client participation in federated learning. [PUB] [PDF]
• SASH: Efficient secure aggregation based on SHPRG for federated learning. [PUB] [PDF]
• Bayesian federated estimation of causal effects from observational data. [PUB] [PDF]

TPAMI

• Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning. [PUB]
• Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning. [PUB] [CODE]

NeurIPS

• Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. [PUB] [PDF]
• LAMP: Extracting Text from Gradients with Language Model Priors. [PUB] [CODE]
• FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. [PUB] [PDF]
• On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. [PUB] [PDF]
• Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. [PUB] [CODE]
• Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. [PUB] [PDF]
• Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. [PUB]
• Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. [PUB] [PDF]
• EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. [PUB] [PDF]
• On-Demand Sampling: Learning Optimally from Multiple Distributions. [PUB] [CODE]
• Improved Utility Analysis of Private CountSketch. [PUB] [PDF] [CODE]
• Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning. [PUB] [CODE]
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. [PUB] [PDF]
• BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. [PUB] [PDF] [CODE]
• Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. [PUB] [PDF]
• Near-Optimal Collaborative Learning in Bandits. [PUB] [PDF] [CODE]
• Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. [PUB] [PDF]
• Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. [PUB] [CODE]
• FedPop: A Bayesian Approach for Personalised Federated Learning. [PUB] [PDF]
• Fairness in Federated Learning via Core-Stability. [PUB] [CODE]
• SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning. [PUB] [PDF]
• FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction. [PUB] [CODE]
• On Sample Optimality in Personalized Collaborative and Federated Learning. [PUB]
• DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. [PUB] [PDF]
• FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. [PUB]
• Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. [PUB] [PDF]
• VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?. [PUB] [CODE]
• DENSE: Data-Free One-Shot Federated Learning. [PUB] [PDF]
• CalFAT: Calibrated Federated Adversarial Training with Label Skewness. [PUB] [PDF]
• SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning. [PUB] [PDF]
• Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning. [PUB] [PDF]
• Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. [PUB]
• Federated Submodel Optimization for Hot and Cold Data Features. [PUB]
• BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [PUB] [PDF]
• Byzantine-tolerant federated Gaussian process regression for streaming data. [PUB] [CODE]
• SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. [PUB] [PDF]
• Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering. [PUB] [PDF] [CODE]
• Communication Efficient Federated Learning for Generalized Linear Bandits. [PUB] [CODE]
• Recovering Private Text in Federated Learning of Language Models. [PUB] [PDF] [CODE]
• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. [PUB] [PDF]
• Global Convergence of Federated Learning for Mixed Regression. [PUB] [PDF]
• Resource-Adaptive Federated Learning with All-In-One Neural Composition. [PUB]
• Self-Aware Personalized Federated Learning. [PUB] [PDF]
• A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. [PUB] [PDF]
• An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. [PUB]
• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. [PUB] [PDF]
• Personalized Online Federated Multi-Kernel Learning. [PUB]
• SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. [PUB] [PDF] [CODE]
• A Unified Analysis of Federated Learning with Arbitrary Client Participation. [PUB] [PDF]
• Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. [PUB] [PDF] [CODE]
• FedSR: A Simple and Effective Domain Generalization Method for Federated Learning. [PUB] [CODE]
• Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [PUB] [PDF] [CODE]
• A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. [PUB] [PDF]
• Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. [PUB]
• On Privacy and Personalization in Cross-Silo Federated Learning. [PUB] [PDF]
• A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. [PUB] [PDF] [CODE]

NeurIPS Datasets and Benchmarks

• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [PUB] [CODE]

ICML

• A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. [PUB] [PDF] [CODE]
• Fast Composite Optimization and Statistical Recovery in Federated Learning. [PUB] [PDF] [CODE]
• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. [PUB] [PDF] [CODE]
• The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning . [PUB] [PDF] [CODE] [SLIDE]
• The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. [PUB] [PDF] [CODE]
• DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. [PUB] [PDF] [CODE]
• FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. [PUB] [PDF] [CODE]
• DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. [PUB] [PDF] [SLIDE] [CODE]
• Accelerated Federated Learning with Decoupled Adaptive Optimization. [PUB] [PDF]
• Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. [PUB] [PDF]
• Multi-Level Branched Regularization for Federated Learning. [PUB] [PDF] [CODE] [PAGE]
• FedScale: Benchmarking Model and System Performance of Federated Learning at Scale . [PUB] [PDF] [CODE]
• Federated Learning with Positive and Unlabeled Data. [PUB] [PDF] [CODE]
• Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. [PUB] [CODE]
• Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. [PUB] [PDF] [CODE]
• Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. [PUB] [PDF] [CODE] [SLIDE] [解读]
• Architecture Agnostic Federated Learning for Neural Networks. [PUB] [PDF] [SLIDE]
• Personalized Federated Learning through Local Memorization. [PUB] [PDF] [CODE]
• Proximal and Federated Random Reshuffling. [PUB] [PDF] [CODE]
• Federated Learning with Partial Model Personalization. [PUB] [PDF] [CODE]
• Generalized Federated Learning via Sharpness Aware Minimization. [PUB] [PDF]
• FedNL: Making Newton-Type Methods Applicable to Federated Learning. [PUB] [PDF] [VIDEO] [SLIDE]
• Federated Minimax Optimization: Improved Convergence Analyses and Algorithms. [PUB] [PDF] [SLIDE]
• Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. [PUB] [PDF] [CODE] [解读]
• FedNest: Federated Bilevel, Minimax, and Compositional Optimization. [PUB] [PDF] [CODE]
• EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. [PUB] [PDF] [CODE]
• Communication-Efficient Adaptive Federated Learning. [PUB] [PDF]
• ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. [PUB] [PDF] [SLIDE] [CODE]
• Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification . [PUB] [PDF] [CODE]
• Anarchic Federated Learning. [PUB] [PDF]
• QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning. [PUB] [CODE]
• Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. [PUB] [PDF]
• Neural Tangent Kernel Empowered Federated Learning. [PUB] [PDF] [CODE]
• Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. [PUB] [PDF]
• Personalized Federated Learning via Variational Bayesian Inference. [PUB] [PDF] [SLIDE] [UC.]
• Federated Learning with Label Distribution Skew via Logits Calibration. [PUB]
• Neurotoxin: Durable Backdoors in Federated Learning. [PUB] [PDF] [CODE]
• Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. [PUB]

ICLR (oral)

• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [PUB] [CODE]

ICLR

• Bayesian Framework for Gradient Leakage. [PUB] [PDF] [CODE]
• Federated Learning from only unlabeled data with class-conditional-sharing clients. [PUB] [CODE]
• FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning. [PUB] [PDF]
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training. [PUB] [PDF] [CODE]
• FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning. [PUB] [PDF] [CODE]
• An Agnostic Approach to Federated Learning with Class Imbalance. [PUB] [CODE]
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization. [PUB] [PDF] [CODE]
• Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models . [PUB] [PDF] [CODE]
• ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. [PUB] [PDF]
• Diverse Client Selection for Federated Learning via Submodular Maximization. [PUB] [CODE]
• Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?. [PUB] [PDF] [CODE]
• Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions . [PUB] [CODE]
• Towards Model Agnostic Federated Learning Using Knowledge Distillation. [PUB] [PDF] [CODE]
• Divergence-aware Federated Self-Supervised Learning. [PUB] [PDF] [CODE]
• What Do We Mean by Generalization in Federated Learning? . [PUB] [PDF] [CODE]
• FedBABU: Toward Enhanced Representation for Federated Image Classification. [PUB] [PDF] [CODE]
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. [PUB] [PDF] [CODE]
• Hybrid Local SGD for Federated Learning with Heterogeneous Communications. [PUB]
• On Bridging Generic and Personalized Federated Learning for Image Classification. [PUB] [PDF] [CODE]
• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [PUB] [PDF]

ICLR Spotlight

• Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [PUB] [PDF] [PAGE] [解读]

2021

JMLR

• One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. [PUB] [CODE]

UAI

• Constrained differentially private federated learning for low-bandwidth devices. [PUB] [PDF]
• Federated stochastic gradient Langevin dynamics. [PUB] [PDF]

ICLR

• Federated Learning Based on Dynamic Regularization. [PUB] [PDF] [CODE]
• Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. [PUB] [PDF]
• HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. [PUB] [PDF] [CODE]
• FedMix: Approximation of Mixup under Mean Augmented Federated Learning. [PUB] [PDF]
• Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms . [PUB] [PDF] [CODE]
• Adaptive Federated Optimization . [PUB] [PDF] [CODE]
• Personalized Federated Learning with First Order Model Optimization. [PUB] [PDF] [CODE] [UC.]
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization . [PUB] [PDF] [CODE]
• FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning. [PUB] [PDF] [CODE]
• Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [PUB] [PDF] [CODE]

ICML

• KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. [PUB] [PDF] [CODE] [解读]
• Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. [PUB] [PDF] [VIDEO] [CODE]
• FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. [PUB] [PDF] [VIDEO]
• Personalized Federated Learning using Hypernetworks . [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读]
• Federated Composite Optimization. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]
• Exploiting Shared Representations for Personalized Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning . [PUB] [PDF] [CODE] [VIDEO]
• Federated Continual Learning with Weighted Inter-client Transfer. [PUB] [PDF] [CODE] [VIDEO]
• Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. [PUB] [PDF] [CODE] [VIDEO]
• Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. [PUB] [PDF] [VIDEO]
• Federated Learning of User Verification Models Without Sharing Embeddings. [PUB] [PDF] [VIDEO]
• Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• Ditto: Fair and Robust Federated Learning Through Personalization. [PUB] [PDF] [CODE] [VIDEO]
• Heterogeneity for the Win: One-Shot Federated Clustering. [PUB] [PDF] [VIDEO]
• The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation . [PUB] [PDF] [CODE] [VIDEO]
• Debiasing Model Updates for Improving Personalized Federated Training. [PUB] [CODE] [VIDEO]
• One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [PUB] [PDF] [CODE] [VIDEO]
• Federated Learning under Arbitrary Communication Patterns. [PUB] [VIDEO]

NeurIPS

• CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. [PUB] [PDF]
• Boosting with Multiple Sources. [PUB]
• DRIVE: One-bit Distributed Mean Estimation. [PUB] [CODE]
• Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. [PUB] [CODE]
• Gradient Inversion with Generative Image Prior. [PUB] [PDF] [CODE]
• Distributed Machine Learning with Sparse Heterogeneous Data. [PUB] [PDF]
• Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. [PUB] [PDF]
• Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. [PUB]
• CAFE: Catastrophic Data Leakage in Vertical Federated Learning. [PUB] [CODE]
• Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. [PUB] [PDF] [CODE]
• Optimality and Stability in Federated Learning: A Game-theoretic Approach. [PUB] [PDF] [CODE]
• QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. [PUB] [PDF] [CODE] [解读]
• The Skellam Mechanism for Differentially Private Federated Learning . [PUB] [PDF] [CODE]
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. [PUB] [PDF]
• STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. [PUB] [PDF]
• Subgraph Federated Learning with Missing Neighbor Generation. [PUB] [PDF] [CODE] [解读]
• Evaluating Gradient Inversion Attacks and Defenses in Federated Learning . [PUB] [PDF] [CODE]
• Personalized Federated Learning With Gaussian Processes. [PUB] [PDF] [CODE]
• Differentially Private Federated Bayesian Optimization with Distributed Exploration. [PUB] [PDF] [CODE]
• Parameterized Knowledge Transfer for Personalized Federated Learning. [PUB] [PDF] [CODE]
• Federated Reconstruction: Partially Local Federated Learning . [PUB] [PDF] [CODE] [UC.]
• Fast Federated Learning in the Presence of Arbitrary Device Unavailability. [PUB] [PDF] [CODE]
• FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. [PUB] [PDF] [CODE]
• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. [PUB] [PDF]
• Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. [PUB] [PDF] [VIDEO]
• Federated Multi-Task Learning under a Mixture of Distributions. [PUB] [PDF] [CODE]
• Federated Graph Classification over Non-IID Graphs. [PUB] [PDF] [CODE] [解读]
• Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. [PUB] [PDF] [CODE]
• On Large-Cohort Training for Federated Learning . [PUB] [PDF] [CODE]
• DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning. [PUB] [PDF] [CODE]
• PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization. [PUB] [VIDEO]
• Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. [PUB] [PDF]
• Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning. [PUB] [PDF] [CODE]
• Federated Linear Contextual Bandits. [PUB] [PDF] [CODE]
• Few-Round Learning for Federated Learning. [PUB]
• Breaking the centralized barrier for cross-device federated learning. [PUB] [CODE] [VIDEO]
• Federated-EM with heterogeneity mitigation and variance reduction. [PUB] [PDF]
• Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. [PUB] [PAGE] [SLIDE]
• FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. [PUB] [PDF] [CODE]

2020

ICLR

• Federated Adversarial Domain Adaptation. [PUB] [PDF] [CODE]
• DBA: Distributed Backdoor Attacks against Federated Learning. [PUB] [CODE]
• Fair Resource Allocation in Federated Learning . [PUB] [PDF] [CODE]
• Federated Learning with Matched Averaging . [PUB] [PDF] [CODE]
• Differentially Private Meta-Learning. [PUB] [PDF]
• Generative Models for Effective ML on Private, Decentralized Datasets . [PUB] [PDF] [CODE]
• On the Convergence of FedAvg on Non-IID Data . [PUB] [PDF] [CODE] [解读]

ICML

• FedBoost: A Communication-Efficient Algorithm for Federated Learning. [PUB] [VIDEO]
• FetchSGD: Communication-Efficient Federated Learning with Sketching. [PUB] [PDF] [VIDEO] [CODE]
• SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. [PUB] [PDF] [VIDEO] [UC.] [解读]
• Federated Learning with Only Positive Labels. [PUB] [PDF] [VIDEO]
• From Local SGD to Local Fixed-Point Methods for Federated Learning. [PUB] [PDF] [SLIDE] [VIDEO]
• Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. [PUB] [PDF] [SLIDE] [VIDEO]

NeurIPS

• Differentially-Private Federated Linear Bandits. [PUB] [PDF] [CODE]
• Federated Principal Component Analysis. [PUB] [PDF] [CODE]
• FedSplit: an algorithmic framework for fast federated optimization. [PUB] [PDF]
• Federated Bayesian Optimization via Thompson Sampling. [PUB] [PDF] [CODE]
• Lower Bounds and Optimal Algorithms for Personalized Federated Learning. [PUB] [PDF]
• Robust Federated Learning: The Case of Affine Distribution Shifts. [PUB] [PDF] [CODE]
• An Efficient Framework for Clustered Federated Learning. [PUB] [PDF] [CODE]
• Distributionally Robust Federated Averaging . [PUB] [PDF] [CODE]
• Personalized Federated Learning with Moreau Envelopes . [PUB] [PDF] [CODE]
• Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. [PUB] [PDF] [UC.]
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. [PUB] [PDF] [CODE] [解读]
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization . [PUB] [PDF] [CODE] [UC.]
• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [PUB] [PDF]
• Federated Accelerated Stochastic Gradient Descent. [PUB] [PDF] [CODE] [VIDEO]
• Inverting Gradients - How easy is it to break privacy in federated learning? . [PUB] [PDF] [CODE]
• Ensemble Distillation for Robust Model Fusion in Federated Learning. [PUB] [PDF] [CODE]
• Throughput-Optimal Topology Design for Cross-Silo Federated Learning. [PUB] [PDF] [CODE]

2019

ICML

• Bayesian Nonparametric Federated Learning of Neural Networks . [PUB] [PDF] [CODE]
• Analyzing Federated Learning through an Adversarial Lens . [PUB] [PDF] [CODE]
• Agnostic Federated Learning. [PUB] [PDF]

2018

NeurIPS

• cpSGD: Communication-efficient and differentially-private distributed SGD. [PUB] [PDF]

2017

NeurIPS

• Federated Multi-Task Learning . [PUB] [PDF] [CODE]

fl in top dm conference and journal

Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and WSDM(Web Search and Data Mining).

• KDD 2025, 2024, 2023(Research Track, Applied Data Science track, Workshop), 2022(Research Track, Applied Data Science track), 2021, 2020
• WSDM 2025, 2024, 2023, 2022, 2021, 2019

fl in top dm conference and journal

2025

KDD

• Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis. [PUB]
• Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. [PUB]
• Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness. [PUB]
• FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning. [PUB]
• Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training. [PUB]
• PraFFL: A Preference-Aware Scheme in Fair Federated Learning. [PUB]
• Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning. [PUB]
• BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning. [PUB]

WSDM

• Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation. [PUB]
• FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning. [PUB]
• Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. [PUB]
• Density-aware and Cluster-based Federated Anomaly Detection on Data Streams. [PUB]
• Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management. [PUB]

2024

KDD Workshop

• FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [PUB]

KDD

• Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination. [PUB]
• BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning. [PUB]
• Federated Graph Learning with Structure Proxy Alignment. [PUB]
• HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning. [PUB]
• FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs. [PUB]
• Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. [PUB]
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning. [PUB]
• On the Convergence of Zeroth-Order Federated Tuning for Large Language Models. [PUB]
• CASA: Clustered Federated Learning with Asynchronous Clients. [PUB]
• FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. [PUB]
• Privacy-Preserving Federated Learning using Flower Framework. [PUB]
• FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning. [PUB]
• FedNLR: Federated Learning with Neuron-wise Learning Rates. [PUB]
• FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model. [PUB]
• FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. [PUB]
• Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning. [PUB]
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection. [PUB]
• FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. [PUB]
• FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction. [PUB]
• OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning. [PUB]
• Personalized Federated Continual Learning via Multi-Granularity Prompt. [PUB]
• Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning. [PUB]
• GPFedRec: Graph-Guided Personalization for Federated Recommendation. [PUB]
• Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. [PUB]
• VertiMRF: Differentially Private Vertical Federated Data Synthesis. [PUB]

WSDM

• User Consented Federated Recommender System Against Personalized Attribute Inference Attack. [PUB] [PDF] [CODE]
• Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning. [PUB]

2023

KDD

• Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation. [PUB] [PDF]
• FedDefender: Client-Side Attack-Tolerant Federated Learning. [PUB] [PDF] [CODE]
• FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity. [PUB] [CODE]
• FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis. [PUB] [PDF]
• ShapleyFL: Robust Federated Learning Based on Shapley Value. [PUB] [CODE]
• Federated Few-shot Learning. [PUB] [PDF] [CODE]
• Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity. [PUB]
• Personalized Federated Learning with Parameter Propagation. [PUB]
• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. [PUB] [PDF] [CODE]
• CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. [PUB] [PDF]
• FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework. [PUB] [PDF]
• FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy. [PUB] [PDF] [CODE]
• Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework. [PUB] [PDF] [CODE]
• DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization. [PUB] [CODE]
• FS-REAL: Towards Real-World Cross-Device Federated Learning. [PUB] [PDF]
• FedMultimodal: A Benchmark for Multimodal Federated Learning. [PUB] [PDF] [CODE]
• PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation. [PUB] [PDF] [NEWS]
• Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. [PUB] [PDF] [CODE]
• UA-FedRec: Untargeted Attack on Federated News Recommendation. [PUB] [PDF] [CODE]

KDD Workshop Summaries

• International Workshop on Federated Learning for Distributed Data Mining. [PUB] [PAGE]

KDD workshop

• Is Normalization Indispensable for Multi-domain Federated Learning?. [PUB]
• Distributed Personalized Empirical Risk Minimization. [PUB]
• Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. [PUB]
• SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. [PUB]
• Optimization of User Resources in Federated Learning for Urban Sensing Applications. [PUB]
• FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. [PUB]
• Federated Graph Analytics with Differential Privacy. [PUB]
• Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. [PUB]
• Uncertainty Quantification in Federated Learning for Heterogeneous Health Data. [PUB]
• A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. [PUB]
• Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. [PUB]
• Federated Blood Supply Chain Demand Forecasting: A Case Study. [PUB]
• Stochastic Clustered Federated Learning. [PUB]
• A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. [PUB]
• Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. [PUB]
• FedNoisy: A Federated Noisy Label Learning Benchmark. [PUB]
• Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. [PUB]
• Federated learning for competing risk analysis in healthcare. [PUB]
• Federated Threat Detection for Smart Home IoT rules. [PUB]

WSDM

• Federated Unlearning for On-Device Recommendation. [PUB] [PDF]

2022

KDD

• Collaboration Equilibrium in Federated Learning. [PUB] [PDF] [CODE]
• Connected Low-Loss Subspace Learning for a Personalization in Federated Learning. [PUB] [PDF] [CODE]
• FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. [PUB]
• Communication-Efficient Robust Federated Learning with Noisy Labels. [PUB] [PDF]
• FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency. [PUB] [PDF] [CODE]
• Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data. [PUB] [PDF] [CODE]
• FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy. [PUB] [PDF]
• Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch. [PUB] [PDF] [解读]
• Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks. [PUB] [PDF]
• No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices. [PUB] [PDF]
• FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling. [PUB] [PDF] [CODE]

KDD (Best Paper Award)

• FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning . [PUB] [PDF] [CODE]

WSDM

• PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [PUB] [PDF]

2021

KDD

• Fed2: Feature-Aligned Federated Learning. [PUB] [PDF]
• FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. [PUB] [CODE]
• Federated Adversarial Debiasing for Fair and Trasnferable Representations. [PUB] [PAGE] [CODE] [SLIDE]
• Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. [PUB] [CODE] [解读]
• AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. [PUB] [PDF]
• FLOP: Federated Learning on Medical Datasets using Partial Networks. [PUB] [PDF] [CODE]

WSDM

• A Practical Federated Learning Framework for Small Number of Stakeholders. [PUB] [CODE]
• Federated Deep Knowledge Tracing. [PUB] [CODE]

2020

KDD

• FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. [PUB] [VIDEO]
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. [PUB] [PDF] [VIDEO]

2019

WSDM

• Federated Online Learning to Rank with Evolution Strategies. [PUB] [CODE]

fl in top secure conference and journal

Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).

• S&P 2025, 2024, 2023, 2022, 2019
• CCS 2024, 2023, 2022, 2021, 2019, 2017
• USENIX Security 2023, 2022, 2020
• NDSS 2025, 2024, 2023, 2022, 2021

fl in top secure conference and journal

2025

S&P

• Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning. [PUB]
• Practical Poisoning Attacks with Limited Byzantine Clients in Clustered Federated Learning. [PUB]

S&P Workshop

• An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models. [PUB]
• Privacy-Preserving Mutual Authentication Protocol for Federated Learning in Intelligent Transportation Systems. [PUB]
• FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning. [PUB]

NDSS

• Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models. [PUB]
• Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction. [PUB] [CODE]
• URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning. [PUB]
• RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation. [PUB] [CODE]

2024

CCS

• Byzantine-Robust Decentralized Federated Learning. [PUB]
• Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation. [PUB]
• Cross-silo Federated Learning with Record-level Personalized Differential Privacy. [PUB]
• Samplable Anonymous Aggregation for Private Federated Data Analysis. [PUB]
• Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy. [PUB]
• Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses. [PUB]
• Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. [PUB]
• Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. [PUB]
• Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. [PUB]

NDSS

• FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting. [PUB]
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning. [PUB]
• Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. [PUB]
• CrowdGuard: Federated Backdoor Detection in Federated Learning. [PUB]

S&P

• Protecting Label Distribution in Cross-Silo Federated Learning. [PUB]
• FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks. [PUB]
• BadVFL: Backdoor Attacks in Vertical Federated Learning. [PUB]
• SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks. [PUB]
• Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation. [PUB]

S&P Workshop

• LayerDBA: Circumventing Similarity-Based Defenses in Federated Learning. [PUB]
• Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. [PUB]
• A Performance Analysis for Confidential Federated Learning. [PUB]

2023

CCS

• Turning Privacy-preserving Mechanisms against Federated Learning. [PUB] [PDF]
• MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. [PUB]
• martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture. [PUB] [PDF] [CODE]
• Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks. [PUB] [PDF]
• Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning. [PUB]
• Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems. [PUB]
• Turning Privacy-preserving Mechanisms against Federated Learning. [PUB] [PDF] [CODE]

USENIX Security

• Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. [PUB] [PDF]
• PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation. [PUB] [CODE]
• Gradient Obfuscation Gives a False Sense of Security in Federated Learning. [PUB] [PDF] [CODE]
• FedVal: Different good or different bad in federated learning. [PUB] [PDF] [CODE]

NDSS

• Securing Federated Sensitive Topic Classification against Poisoning Attacks. [PUB] [PDF] [CODE]
• PPA: Preference Profiling Attack Against Federated Learning. [PUB] [PDF]

S&P

• FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. [PUB] [PDF]
• Scalable and Privacy-Preserving Federated Principal Component Analysis. [PUB] [PDF]
• BayBFed: Bayesian Backdoor Defense for Federated Learning. [PUB] [PDF]
• 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning. [PUB] [CODE]
• RoFL: Robustness of Secure Federated Learning. [PUB] [PDF] [CODE]
• Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. [PUB] [CODE]
• ELSA: Secure Aggregation for Federated Learning with Malicious Actors.
• Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. [PUB] [PDF]

S&P Workshop

• SafeFL: MPC-friendly Framework for Private and Robust Federated Learning. [PUB]
• On the Pitfalls of Security Evaluation of Robust Federated Learning. [PUB]

2022

CCS

• CERBERUS: Exploring Federated Prediction of Security Events. [PUB] [PDF]
• EIFFeL: Ensuring Integrity for Federated Learning. [PUB] [PDF]
• Eluding Secure Aggregation in Federated Learning via Model Inconsistency. [PUB] [PDF] [CODE]
• Federated Boosted Decision Trees with Differential Privacy. [PUB] [PDF] [CODE]

S&P

• Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. [PUB] [VIDEO]

USENIX Security

• SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors. [PUB] [SLIDE] [VIDEO]
• Label Inference Attacks Against Vertical Federated Learning. [PUB] [SLIDE] [CODE] [VIDEO]
• FLAME: Taming Backdoors in Federated Learning. [PUB] [SLIDE] [PDF] [VIDEO]

NDSS

• Local and Central Differential Privacy for Robustness and Privacy in Federated Learning. [PUB] [PDF] [VIDEO] [UC.]
• Interpretable Federated Transformer Log Learning for Cloud Threat Forensics. [PUB] [VIDEO] [UC.]
• FedCRI: Federated Mobile Cyber-Risk Intelligence. [PUB] [VIDEO]
• DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [PUB] [PDF] [VIDEO]

2021

CCS

• Private Hierarchical Clustering in Federated Networks. [PUB] [PDF]

NDSS

• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]
• POSEIDON: Privacy-Preserving Federated Neural Network Learning. [PUB] [VIDEO]
• Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. [PUB] [CODE] [VIDEO]

S&P Workshop

• SAFELearn: Secure Aggregation for private FEderated Learning. [PUB]

2020

USENIX Security

• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]

2019

CCS (Poster)

• A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [PUB]

S&P Workshop

• IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning. [PUB]

S&P

• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning . [PUB] [VIDEO] [SLIDE] [CODE]

2017

CCS

• Practical Secure Aggregation for Privacy Preserving Machine Learning. [PUB] [PDF] [解读] [UC.] [UC]

fl in top cv conference and journal

Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia), IJCV(International Journal of Computer Vision).

• CVPR 2025, 2024, 2023, 2022, 2021
• ICCV 2023, 2021
• ECCV 2024, 2022, 2020
• MM 2024, 2023, 2022, 2021, 2020
• IJCV 2025, 2024

fl in top cv conference and journal

2025

CVPR

• Federated Learning with Domain Shift Eraser. [PUB]
• Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning. [PUB] [CODE]
• FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models. [PUB] [CODE]
• FedCS: Coreset Selection for Federated Learning. [PUB]
• AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models. [PUB]
• NoT: Federated Unlearning via Weight Negation. [PUB]
• Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution. [PUB]
• Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning. [PUB]
• Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch. [PUB] [CODE]
• Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning. [PUB]
• HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. [PUB] [COCE]
• F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics. [PUB] [PAGE]
• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors. [PUB]
• FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity. [PUB] [CODE]
• Population Normalization for Federated Learning. [PUB]
• Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. [PUB] [CODE]
• dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis. [PUB]
• Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection. [PUB] [CODE]
• A Simple Data Augmentation for Feature Distribution Skewed Federated Learning. [PUB] [CODE]
• Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor. [PUB]
• Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning. [PUB] [CODE]
• pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation. [PUB]
• FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning. [PUB]
• Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter. [PUB]
• FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [PUB] [CODE]
• Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model. [PUB]

IJCV

• Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning. [PUB]

2024

MM

• DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations. [PUB]
• One-shot-but-not-degraded Federated Learning. [PUB]
• Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning. [PUB]
• FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models. [PUB]
• Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition. [PUB]
• CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation. [PUB]
• Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training. [PUB]
• FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework. [PUB]
• One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity. [PUB]
• FedSLS: Exploring Federated Aggregation in Saliency Latent Space. [PUB]
• Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia. [PUB]
• FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning. [PUB]
• Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data. [PUB]
• Cross-Modal Meta Consensus for Heterogeneous Federated Learning. [PUB]
• Masked Random Noise for Communication-Efficient Federated Learning. [PUB]
• Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations. [PUB]
• Adaptive Hierarchical Aggregation for Federated Object Detection. [PUB]
• FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement. [PUB]
• Federated Fuzzy C-means with Schatten-p Norm Minimization. [PUB]
• Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation. [PUB]

IJCV

• Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification. [PUB]

ECCV

• SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks. [PUB] [CODE]
• FedHide: Federated Learning by Hiding in the Neighbors. [PUB]
• FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation. [PUB]
• FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients. [PUB]
• Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning. [PUB]
• Federated Learning with Local Openset Noisy Labels. [PUB]
• FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. [PUB]
• Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection. [PUB]
• BAFFLE: A Baseline of Backpropagation-Free Federated Learning. [PUB]
• PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning. [PUB]
• Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning. [PUB]
• Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. [PUB]
• FedHARM: Harmonizing Model Architectural Diversity in Federated Learning. [PUB]
• SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. [PUB]
• Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. [PUB]
• Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning. [PUB]
• Towards Multi-modal Transformers in Federated Learning. [PUB]
• Local and Global Flatness for Federated Domain Generalization. [PUB]
• Feature Diversification and Adaptation for Federated Domain Generalization. [PUB]
• PFEDEDIT: Personalized Federated Learning via Automated Model Editing. [PUB]

CVPR

• FedHCA2: Towards Hetero-Client Federated Multi-Task Learning. [PUB] [SUPP] [PDF] [CODE]
• Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity. [PUB] [PDF] [CODE]
• Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts. [PUB] [SUPP] [PDF] [CODE]
• FedMef: Towards Memory-efficient Federated Dynamic Pruning. [PUB] [SUPP] [PDF]
• Communication-Efficient Federated Learning with Accelerated Client Gradient. [PUB] [SUPP] [PDF] [CODE]
• Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [PUB] [SUPP] [CODE]
• Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning. [PUB] [SUPP] [CODE]
• Towards Efficient Replay in Federated Incremental Learning. [PUB] [SUPP] [PDF]
• Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices. [PUB] [SUPP] [PDF]
• Data Valuation and Detections in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Decentralized Directed Collaboration for Personalized Federated Learning. [PUB] [SUPP] [PDF]
• Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. [PUB] [SUPP] [PDF] [CODE]
• Relaxed Contrastive Learning for Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [PUB] [SUPP] [PDF] [VIDEO]
• Traceable Federated Continual Learning. [PUB] [SUPP] [CODE]
• Federated Online Adaptation for Deep Stereo. [PUB] [SUPP] [PDF] [CODE] [PAGE] [VIDEO]
• Federated Generalized Category Discovery. [PUB] [SUPP] [PDF] [CODE]
• Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization. [PUB] [SUPP] [PDF] [CODE]
• Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. [PUB] [SUPP] [PDF] [CODE]
• PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees. [PUB] [SUPP] [PDF] [CODE]
• FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• FedUV: Uniformity and Variance for Heterogeneous Federated Learning. [PUB] [SUPP] [PDF]
• FedAS: Bridging Inconsistency in Personalized Federated Learning. [PUB] [CODE]
• FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning. [PUB] [SUPP] [PDF] [CODE]
• Device-Wise Federated Network Pruning. [PUB] [SUPP]
• Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping. [PUB] [SUPP]
• DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning. [PUB] [SUPP] [PDF]
• An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning. [PUB] [SUPP] [PDF] [CODE] [POSTER] [SLIDES]
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity. [PUB] [SUPP] [PDF]
• FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning. [PUB] [SUPP] [CODE] [PAGE] [POSTER] [VIDEO]

CVPR workshop

• Collaborative Visual Place Recognition through Federated Learning. [PUB] [SUPP] [PDF]
• FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer. [PUB] [SUPP] [PDF]
• Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights. [PUB]
• On the Efficiency of Privacy Attacks in Federated Learning. [PUB] [PDF]

2023

MM

• FedCE: Personalized Federated Learning Method based on Clustering Ensembles. [PUB]
• FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes. [PUB]
• Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor. [PUB] [PDF] [CODE]
• Federated Deep Multi-View Clustering with Global Self-Supervision. [PUB] [PDF]
• FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy. [PUB]
• Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing. [PUB] [CODE]
• Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data. [PUB] [PDF]
• FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data. [PUB]
• Federated Learning with Label-Masking Distillation. [PUB] [CODE]
• Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data. [PUB] [PDF] [CODE]
• A Four-Pronged Defense Against Byzantine Attacks in Federated Learning. [PUB] [PDF]
• Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning. [PUB]
• FedGH: Heterogeneous Federated Learning with Generalized Global Header. [PUB] [PDF] [CODE]
• Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation. [PUB] [PDF] [CODE]
• AffectFAL: Federated Active Affective Computing with Non-IID Data. [PUB] [CODE]
• Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation. [PUB]

ICCV

• Towards Attack-tolerant Federated Learning via Critical Parameter Analysis. [PUB] [PDF] [CODE] [SUPP]
• Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation. [PUB] [PDF] [SUPP]
• Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning. [PUB] [CODE] [SUPP]
• GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning. [PUB] [PDF] [CODE] [SUPP]
• Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization. [PUB] [SUPP]
• PGFed: Personalize Each Client's Global Objective for Federated Learning. [PUB] [PDF] [CODE] [SUPP]
• FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning. [PUB] [PDF] [CODE] [SUPP]
• L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. [PUB] [PDF] [SUPP]
• FedPD: Federated Open Set Recognition with Parameter Disentanglement. [PUB] [CODE]
• TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation. [PUB] [PDF] [CODE]
• Towards Instance-adaptive Inference for Federated Learning. [PUB] [PDF] [CODE]
• Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence. [PUB] [PDF] [CODE]
• zPROBE: Zero Peek Robustness Checks for Federated Learning. [PUB] [PDF] [SUPP]
• ProtoFL: Unsupervised Federated Learning via Prototypical Distillation. [PUB] [PDF]
• MAS: Towards Resource-Efficient Federated Multiple-Task Learning. [PUB] [PDF] [CODE] [SUPP]
• FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation. [PUB] [PDF] [SUPP]
• When Do Curricula Work in Federated Learning?. [PUB] [PDF] [SUPP]
• Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples. [PUB] [PDF] [CODE]
• Multi-Metrics Adaptively Identifies Backdoors in Federated Learning. [PUB] [PDF] [CODE] [SUPP]
• No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier. [PUB] [PDF] [CODE] [SUPP]
• FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation. [PUB] [PDF] [SUPP]
• Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration. [PUB] [PDF] [CODE] [SUPP]
• Global Balanced Experts for Federated Long-Tailed Learning. [PUB] [CODE] [SUPP]
• Knowledge-Aware Federated Active Learning with Non-IID Data. [PUB] [PDF] [CODE] [SUPP]
• Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation. [PUB] [SUPP]
• Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels. [PUB] [PDF] [SUPP]
• Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat. [PUB] [PDF] [CODE] [SUPP]
• Robust Heterogeneous Federated Learning under Data Corruption. [PUB] [CODE] [SUPP]
• Personalized Semantics Excitation for Federated Image Classification. [PUB] [CODE]
• Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology. [PUB] [PDF] [CODE] [SUPP]

ICCV workshop

• Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. [PUB] [PDF]
• Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. [PUB]
• FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. [PUB] [CODE]
• FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. [PUB]

CVPR

• Rethinking Federated Learning With Domain Shift: A Prototype View. [PUB] [CODE]
• Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning. [PUB] [CODE]
• DaFKD: Domain-Aware Federated Knowledge Distillation. [PUB] [CODE]
• The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning. [PUB] [PDF]
• FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation. [PUB]
• On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data. [PUB] [PDF]
• Elastic Aggregation for Federated Optimization. [PUB]
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. [PUB] [PDF]
• Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity. [PUB]
• ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients. [PUB] [CODE]
• Reliable and Interpretable Personalized Federated Learning. [PUB]
• Federated Domain Generalization With Generalization Adjustment. [PUB] [CODE]
• Make Landscape Flatter in Differentially Private Federated Learning. [PUB] [PDF] [CODE]
• Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. [PUB] [PDF] [CODE]
• STDLens: Model Hijacking-Resilient Federated Learning for Object Detection. [PUB] [PDF] [CODE]
• Re-Thinking Federated Active Learning Based on Inter-Class Diversity. [PUB] [PDF] [CODE]
• Learning Federated Visual Prompt in Null Space for MRI Reconstruction. [PUB] [PDF] [CODE]
• Fair Federated Medical Image Segmentation via Client Contribution Estimation. [PUB] [PDF] [CODE]
• Federated Learning With Data-Agnostic Distribution Fusion. [PUB] [CODE]
• How To Prevent the Poor Performance Clients for Personalized Federated Learning?. [PUB]
• GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting. [PUB] [PDF] [CODE]
• Bias-Eliminating Augmentation Learning for Debiased Federated Learning. [PUB]
• Federated Incremental Semantic Segmentation. [PUB] [PDF] [CODE]

CVPR workshop

• Asynchronous Federated Continual Learning. [PUB] [PDF] [SILDES] [CODE]
• Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks. [PUB] [CODE]
• OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework. [PUB] [PDF] [CODE]
• Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data. [PUB] [SUPP] [PDF] [CODE]
• TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training. [PUB] [PDF]
• Many-Task Federated Learning: A New Problem Setting and a Simple Baseline. [PUB] [CODE]

2022

MM

• Confederated Learning: Going Beyond Centralization. [PUB]
• Few-Shot Model Agnostic Federated Learning. [PUB] [CODE]
• Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation. [PUB]

ECCV

• FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks. [PUB] [SUPP]
• Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation. [PUB] [SUPP] [PDF] [CODE]
• Improving Generalization in Federated Learning by Seeking Flat Minima. [PUB] [SUPP] [PDF] [CODE]
• AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation. [PUB] [SUPP] [PDF] [CODE] [PAGE]
• SphereFed: Hyperspherical Federated Learning. [PUB] [SUPP] [PDF]
• Federated Self-Supervised Learning for Video Understanding. [PUB] [PDF] [CODE]
• FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation. [PUB] [SUPP] [PDF] [CODE]
• Addressing Heterogeneity in Federated Learning via Distributional Transformation. [PUB] [CODE]
• FedX: Unsupervised Federated Learning with Cross Knowledge Distillation. [PUB] [SUPP] [PDF] [CODE]
• Personalizing Federated Medical Image Segmentation via Local Calibration. [PUB] [SUPP] [PDF] [CODE]

CVPR

• ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework. [PUB]
• Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. [PUB] [SUPP] [PDF] [CODE] [VIDEO]
• FedCorr: Multi-Stage Federated Learning for Label Noise Correction. [PUB] [SUPP] [PDF] [CODE] [VIDEO]
• FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning. [PUB] [SUPP] [PDF]
• Layer-Wised Model Aggregation for Personalized Federated Learning. [PUB] [SUPP] [PDF]
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Federated Learning With Position-Aware Neurons. [PUB] [SUPP] [PDF]
• RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning. [PUB] [SUPP] [PDF] [CODE]
• Learn From Others and Be Yourself in Heterogeneous Federated Learning. [PUB] [CODE] [VIDEO]
• Robust Federated Learning With Noisy and Heterogeneous Clients. [PUB] [SUPP] [CODE]
• ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction. [PUB] [PDF] [CODE] [解读]
• Federated Class-Incremental Learning. [PUB] [PDF] [CODE]
• Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning. [PUB] [PDF]
• Differentially Private Federated Learning With Local Regularization and Sparsification. [PUB] [PDF]
• Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage. [PUB] [PDF] [CODE] [VIDEO]
• CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning. [PUB] [PDF]
• Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation. [PUB] [PDF]

CVPR workshop

• Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning. [PUB] [PDF] [SILDES] [VIDEO]
• MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients. [PUB] [PDF] [SILDES] [VIDEO]
• Communication-Efficient Federated Data Augmentation on Non-IID Data. [PUB]
• Does Federated Dropout Actually Work?. [PUB] [VIDEO]
• FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication. [PUB] [SLIDES] [VIDEO]

2021

CVPR

• Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning. [PUB] [PDF] [CODE]
• Model-Contrastive Federated Learning . [PUB] [PDF] [CODE] [解读]
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space . [PUB] [PDF] [CODE]
• Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective. [PUB] [PDF] [CODE]

ICCV

• Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment. [PUB]
• Ensemble Attention Distillation for Privacy-Preserving Federated Learning. [PUB] [PDF]
• Collaborative Unsupervised Visual Representation Learning from Decentralized Data. [PUB] [PDF]

MM

• Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification. [PUB] [PDF]

2020

ECCV

• Federated Visual Classification with Real-World Data Distribution. [PUB] [PDF] [VIDEO]

MM

• InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages. [PUB]
• Performance Optimization of Federated Person Re-identification via Benchmark Analysis data.. [PUB] [PDF] [CODE] [解读]

fl in top nlp conference and journal

Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).

• ACL 2025, 2024, 2023, 2022, 2021, 2019
• NAACL 2024, 2022, 2021
• EMNLP 2024, 2023, 2022, 2021, 2020
• COLING 2025, 2020

fl in top nlp conference and journal

2025

ACL

• Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients. [PUB]
• FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models. [PUB]

ACL Findings

• Federated Data-Efficient Instruction Tuning for Large Language Models. [PUB]
• FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models. [PUB]
• Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models. [PUB]
• FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration. [PUB]

COLING

• Gradient Inversion Attack in Federated Learning: Exposing Text Data through Discrete Optimization. [PUB]
• FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models. [PUB]
• Federated Incremental Named Entity Recognition. [PUB]
• FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning. [PUB]

COLING (Industry)

• Federated Retrieval Augmented Generation for Multi-Product Question Answering. [PUB]

2024

EMNLP

• A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems. [PUB]
• Safely Learning with Private Data: A Federated Learning Framework for Large Language Model. [PUB]
• FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models. [PUB]
• Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models. [PUB]
• Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models. [PUB]

EMNLP Findings

• Promoting Data and Model Privacy in Federated Learning through Quantized LoRA. [PUB]
• Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models. [PUB]

NAACL

• Generalizable Multilingual Hate Speech Detection on Low Resource Indian Languages using Fair Selection in Federated Learning. [PUB]
• Open-Vocabulary Federated Learning with Multimodal Prototyping. [PUB]
• Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning. [PUB]

NAACL Findings

• FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering. [PUB]
• Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning. [PUB]
• Can Public Large Language Models Help Private Cross-device Federated Learning?. [PUB]

ACL Findings

• Fair Federated Learning with Biased Vision-Language Models. [PUB]

2023

EMNLP

• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization. [PUB] [PDF] [CODE]
• Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification. [PUB] [CODE]
• FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models. [PUB] [CODE]
• FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning. [PUB] [PDF]

EMNLP industry Track

• Coordinated Replay Sample Selection for Continual Federated Learning. [PUB] [PDF]

EMNLP Findings

• Tunable Soft Prompts are Messengers in Federated Learning. [PUB] [PDF] [CODE]

ACL

• Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms. [PUB] [PDF] [CODE]
• FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP. [PUB] [CODE]

ACL Findings

• Client-Customized Adaptation for Parameter-Efficient Federated Learning. [PUB]
• Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter. [PUB] [PDF] [CODE]
• Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets. [PUB]
• FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models. [PUB]

ACL Industry Track

• Federated Learning of Gboard Language Models with Differential Privacy. [PUB] [PDF]

2022

EMNLP

• Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling. [PUB] [PDF]
• A Federated Approach to Predicting Emojis in Hindi Tweets. [PUB] [PDF] [CODE]
• Federated Model Decomposition with Private Vocabulary for Text Classification. [PUB] [CODE]
• Fair NLP Models with Differentially Private Text Encoders. [PUB] [PDF] [CODE]

EMNLP Findings

• Federated Continual Learning for Text Classification via Selective Inter-client Transfer. [PUB] [PDF] [CODE]
• Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg.. [PUB] [PDF] [CODE]
• Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation. [PUB] [PDF]

ACL workshop

• Scaling Language Model Size in Cross-Device Federated Learning. [PUB] [PDF]
• Intrinsic Gradient Compression for Scalable and Efficient Federated Learning. [PUB] [PDF]
• ActPerFL: Active Personalized Federated Learning. [PUB] [PAGE]

NAACL

• FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks . [PUB] [PDF] [CODE]
• Federated Learning with Noisy User Feedback. [PUB] [PDF]
• Training Mixed-Domain Translation Models via Federated Learning. [PUB] [PAGE] [PDF]
• Pretrained Models for Multilingual Federated Learning. [PUB] [PDF] [CODE]

2021

ACL workshop

• Federated Chinese Word Segmentation with Global Character Associations. [PUB] [CODE]

EMNLP

• Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation. [PUB] [PDF] [CODE] [VIDEO]
• Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories. [PUB] [CODE] [VIDEO]
• A Secure and Efficient Federated Learning Framework for NLP. [PUB] [PDF] [VIDEO]

EMNLP workshop

• Distantly Supervised Relation Extraction in Federated Settings. [PUB] [PDF] [CODE]

NAACL workshop

• Federated Learning with Noisy User Feedback. [PUB] [PDF]
• An Investigation towards Differentially Private Sequence Tagging in a Federated Framework. [PUB]
• Understanding Unintended Memorization in Language Models Under Federated Learning. [PUB] [PDF]

2020

EMNLP

• FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction. [PUB] [VIDEO] [解读]

EMNLP workshop

• Empirical Studies of Institutional Federated Learning For Natural Language Processing. [PUB]

COLING

• Federated Learning for Spoken Language Understanding. [PUB]

2019

ACL workshop

• Two-stage Federated Phenotyping and Patient Representation Learning. [PUB] [PDF] [CODE] [UC.]

fl in top ir conference and journal

Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

• SIGIR 2025, 2024, 2023, 2022, 2021, 2020

fl in top ir conference and journal

2025

SIGIR

• FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation. [PUB]
• NodeRec+: A Lightweight Framework for Federated Recommender Systems. [PUB]
• Unlearning for Federated Online Learning to Rank: A Reproducibility Study. [PUB]
• Joint Item Embedding Dual-view Exploration and Adaptive Local-Global Fusion for Federated Recommendation. [PUB]

2024

SIGIR

• ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit. [PUB]
• Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport. [PUB]
• FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation. [PUB] [PDF] [CODE]
• FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction. [PUB]

2023

SIGIR

• Personalized Federated Relation Classification over Heterogeneous Texts. [PUB]
• Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity. [PUB]
• Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures. [PUB] [PDF]
• FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning. [PUB] [PDF] [CODE]
• Edge-cloud Collaborative Learning with Federated and Centralized Features (short-paper). [PUB] [PDF]
• FLIRT: Federated Learning for Information Retrieval (extended-abstract). [PUB]

2022

SIGIR

• Is Non-IID Data a Threat in Federated Online Learning to Rank?. [PUB] [CODE]

2021

SIGIR

• FedCT: Federated Collaborative Transfer for Recommendation. [PUB] [PDF] [CODE]
• On the Privacy of Federated Pipelines. [PUB]
• FedCMR: Federated Cross-Modal Retrieval. [PUB] [CODE]

2020

SIGIR

• Meta Matrix Factorization for Federated Rating Predictions. [PUB] [PDF]

fl in top db conference and journal

Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).

• SIGMOD 2022, 2021
• ICDE 2025, 2024, 2023, 2022, 2021
• VLDB 2025, 2024, 2023, 2022, 2021, 2021, 2020

fl in top db conference and journal

2025

VLDB

• PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning. [PUB] [CODE]
• Federated Incomplete Tabular Data Prediction with Missing Complementarity. [PUB] [CODE]
• Federated and Balanced Clustering for High-Dimensional Data. [PUB] [CODE]
• FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases. [PUB]
• GORAM: Graph-Oriented ORAM for Efficient Ego-Centric Queries on Federated Graphs. [PUB] [CODE]
• Federated Data Distribution Shift Estimation. [PUB] [CODE]
• OpenFGL: A Comprehensive Benchmark for Federated Graph Learning. [PUB] [CODE]

ICDE

• A Bargaining-Based Approach for Feature Trading in Vertical Federated Learning. [PUB]
• pFSSL-D: Generalization Meets Personalization in Dual-Phase Federated Semi-Supervised Learning. [PUB]
• FedEcover: Fast and Stable Converging Model-Heterogeneous Federated Learning with Efficient-Coverage Submodel Extraction. [PUB]
• Federated Trajectory Similarity Learning with Privacy-Preserving Clustering. [PUB]
• FedSDP: Federated Self-Derived Prototypes for Personalized Federated Learning. [PUB]
• Federated Data Analytics with Differentially Private Density Estimation Model. [PUB]
• Efficient Data Valuation Approximation in Federated Learning: A Sampling-Based Approach. [PUB]
• pFedAFM: Adaptive Feature Mixture for Data-Level Personalization in Heterogeneous Federated Learning on Mobile Edge Devices. [PUB]
• Heterogeneous-Aware Traffic Prediction: A Privacy-Preserving Federated Learning Framework. [PUB]
• Online Federated Learning on Distributed Unknown Data Using UAVs. [PUB]
• Hounding Data Diversity: Towards Participant Selection in Vertical Federated Learning. [PUB]

2024

ICDE

• FedMix: Boosting with Data Mixture for Vertical Federated Learning. [PUB]
• FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation. [PUB]
• Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning. [PUB]
• Semi-Asynchronous Online Federated Crowdsourcing. [PUB]
• AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity. [PUB]
• MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation. [PUB]
• LightTR: A Lightweight Framework for Federated Trajectory Recovery. [PUB]
• Feed: Towards Personalization-Effective Federated Learning. [PUB]
• Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization. [PUB]
• Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning. [PUB]
• HeteFedRec: Federated Recommender Systems with Model Heterogeneity. [PUB]
• Hide Your Model: A Parameter Transmission-free Federated Recommender System. [PUB]
• FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query. [PUB]
• Preventing the Popular Item Embedding Based Attack in Federated Recommendations. [PUB]
• RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection. [PUB]

DEEM@SIGMOD

• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly. [PUB]

VLDB

• FedSQ: A Secure System for Federated Vector Similarity Queries. [PUB]
• FedSM: A Practical Federated Shared Mobility System. [PUB]
• OFL-W3: A One-Shot Federated Learning System on Web 3.0. [PUB]
• Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation. [PUB]
• Uldp-FL: Federated Learning with Across Silo User-Level Differential Privacy. [PUB]
• Performance-Based Pricing of Federated Learning via Auction. [PUB] [CODE]
• A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer. [PUB] [CODE]
• Communication Efficient and Provable Federated Unlearning. [PUB] [PDF] [CODE]

2023

ICDE

• Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices. [PUB]
• Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs. [PUB] [CODE]
• FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge. [PUB] [PDF]
• Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices. [PUB] [PDF]
• Federated IoT Interaction Vulnerability Analysis. [PUB]
• Distribution-Regularized Federated Learning on Non-IID Data. [PUB]
• Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data. [PUB] [CODE]
• FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration. [PUB]

VLDB

• FedGTA: Topology-aware Averaging for Federated Graph Learning. [PUB] [CODE]
• FS-Real: A Real-World Cross-Device Federated Learning Platform. [PUB] [PDF] [CODE]
• Federated Calibration and Evaluation of Binary Classifiers. [PUB] [PDF] [CODE]
• Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. [PUB] [PDF] [CODE]
• Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. [PUB] [CODE]
• Differentially Private Vertical Federated Clustering. [PUB] [PDF] [CODE]
• FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. . [PUB] [PDF] [CODE]
• Secure Shapley Value for Cross-Silo Federated Learning. [PUB] [PDF] [CODE]

2022

VLDB

• OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization. [PUB] [PDF] [CODE]
• Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. [PUB] [CODE]
• Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update. [PUB] [PDF] [CODE]
• FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification. [PUB] [CODE]

ICDE

• Improving Fairness for Data Valuation in Horizontal Federated Learning. [PUB] [PDF]
• FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity. [PUB] [PDF] [CODE]
• FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. [PUB]
• Federated Learning on Non-IID Data Silos: An Experimental Study. . [PUB] [PDF] [CODE]
• Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing. [PUB]
• Samba: A System for Secure Federated Multi-Armed Bandits. [PUB] [CODE]
• FedRecAttack: Model Poisoning Attack to Federated Recommendation. [PUB] [PDF] [CODE]
• Enhancing Federated Learning with In-Cloud Unlabeled Data. [PUB]
• Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning. [PUB]

SIGMOD Tutorial

• An Introduction to Federated Computation. [PUB]

SIGMOD

• BlindFL: Vertical Federated Machine Learning without Peeking into Your Data. [PUB] [PDF]

2021

ICDE

• An Efficient Approach for Cross-Silo Federated Learning to Rank. [PUB] [RELATED PAPER(ZH)]
• Feature Inference Attack on Model Predictions in Vertical Federated Learning. [PUB] [PDF] [CODE]
• Efficient Federated-Learning Model Debugging. [PUB]

VLDB

• Federated Matrix Factorization with Privacy Guarantee. [PUB]
• Projected Federated Averaging with Heterogeneous Differential Privacy. [PUB] [CODE]
• Enabling SQL-based Training Data Debugging for Federated Learning. [PUB] [PDF] [CODE]
• Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain. [PUB]
• Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks. [PUB] [VIDEO]

SIGMOD

• VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning. [PUB]
• ExDRa: Exploratory Data Science on Federated Raw Data. [PUB]

SIGMOD workshop

• Joint blockchain and federated learning-based offloading in harsh edge computing environments. [PUB]

2020

VLDB

• Privacy Preserving Vertical Federated Learning for Tree-based Models. [PUB] [PDF] [VIDEO] [CODE]

fl in top network conference and journal

Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).

• SIGCOMM 2025
• INFOCOM 2025, 2024, 2023, 2022(Page), 2021(Page), 2020(Page), 2019, 2018
• MobiCom 2024, 2023, 2022, 2021, 2020
• NSDI 2025, 2023(Spring, Fall)
• WWW 2025, 2024, 2023, 2022, 2021

fl in top network conference and journal

2025

SIGCOMM (Posters and Demos)

• A Lightweight Emulation Framework for Energy-Aware Federated Learning. [PUB]
• NEBULA - Decentralized Federated Learning for Heterogeneous Networks. [PUB]
• Federated Inference: Towards Collaborative and Privacy-Preserving Inference over Edge Devices. [PUB]

INFOCOM

• Preference Profiling Attacks Against Vertical Federated Learning Over Graph Data. [PUB]
• FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection. [PUB]
• Federated Adaptive Fine-Tuning of Large Language Models with Heterogeneous Quantization and LoRA. [PUB]
• FLM-TopK: Expediting Federated Large Language Model Tuning by Sparsifying Intervalized Gradients. [PUB]
• Client Sampling for Communication-Efficient Distributed Minimax Optimization. [PUB]
• Robust Contextual Combinatorial Multi-Armed Bandits for Unreliable Network Systems. [PUB]
• ElasticFed: Collaborative Large-Small Transformer Training for Federated Continual Learning at Edge. [PUB]
• γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning. [PUB]
• PSFL: Parallel-Sequential Federated Learning with Convergence Guarantees. [PUB]
• GraphRx: Graph-Based Collaborative Learning Among Multiple Cells for Uplink Neural Receivers. [PUB]
• Input Integrity and Authentic Results: Towards Trustworthy Aggregation in Federated Learning. [PUB]
• Lightweight Federated Learning with Differential Privacy and Straggler Resilience. [PUB]
• Communication Efficient Asynchronous Stochastic Gradient Descent. [PUB]
• GeoFL: A Framework for Efficient Geo-Distributed Cross-Device Federated Learning. [PUB]
• FedPDA: Collaborative Learning for Reducing Online-Adaptation Frequency of Neural Receivers. [PUB]
• LCO-AGQ: A Lightweight Client-Oriented Adaptive Gradient Quantization Algorithm for Federated Learning. [PUB]
• FedEXT: Differential Federated Learning with Complementary Extension of Edge Models. [PUB]
• FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching. [PUB]
• Similarity-Guided Rapid Deployment of Federated Intelligence Over Heterogeneous Edge Computing. [PUB]
• CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters. [PUB]
• Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information. [PUB]
• Multi-Task Reinforcement Learning for Collaborative Network Optimization in Data Centers. [PUB]
• Towards Federated Inference: An Online Model Ensemble Framework for Cooperative Edge AI. [PUB]
• VaniKG: Vanishing Key Gradient Attack and Defense for Robust Federated Aggregation. [PUB]
• Combating Deep Leakage from Gradients in Cross-Silo Federated Learning with QKD. [PUB]
• FedUFD: Personalized Edge Computing Using Federated Uncertainty-Driven Feature Distillation. [PUB]
• Accelerating Clustered Federated Learning in Dynamic D2D Networks with Transferable GNN. [PUB]
• AoI-Aware Federated Unlearning for Streaming Data with Online Client Selection and Pricing. [PUB]

WWW

• Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation. [PUB]
• Empowering Federated Graph Rationale Learning with Latent Environments. [PUB]
• Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems against Attribute Inference Attacks. [PUB]
• P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network. [PUB]
• Local Differentially Private Release of Infinite Streams With Temporal Relevance. [PUB]
• Unlearning Incentivizes Learning under Privacy Risk. [PUB]
• Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training. [PUB]
• Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges. [PUB]
• Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing. [PUB]
• Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model. [PUB]
• Federated Graph Anomaly Detection via Disentangled Representation Learning. [PUB]
• FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities. [PUB]
• Subgraph Federated Unlearning. [PUB]
• Personalized Federated Recommendation for Cold-Start Users via Adaptive Knowledge Fusion. [PUB]
• NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning. [PUB]
• FedRIR: Rethinking Information Representation in Federated Learning. [PUB]
• PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning. [PUB]
• Provably Robust Federated Reinforcement Learning. [PUB]
• MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. [PUB]
• FLock: Robust and Privacy-Preserving Federated Learning based on Practical Blockchain State Channels. [PUB]

NSDI

• PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality. [PUB] [SLIDE]

2024

INFOCOM

• Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning. [PUB]
• Strategic Data Revocation in Federated Unlearning. [PUB]
• FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding. [PUB]
• Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization. [PUB]
• FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value. [PUB]
• DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service. [PUB]
• Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization. [PUB]
• BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency. [PUB]
• Titanic: Towards Production Federated Learning with Large Language Models. [PUB]
• Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression. [PUB]
• Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes. [PUB]
• Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications. [PUB]
• GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy. [PUB]
• Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration. [PUB] [CODE]
• AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation. [PUB]
• Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning. [PUB]
• Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks. [PUB]
• Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency. [PUB]
• Federated Offline Policy Optimization with Dual Regularization. [PUB]
• A Semi-Asynchronous Decentralized Federated Learning Framework via Tree-Graph Blockchain. [PUB]
• SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation. [PUB]
• Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments. [PUB]

INFOCOM workshop

• Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks. [PUB]
• Decentralized Federated Learning Under Free-riders: Credibility Analysis. [PUB]
• TrustBandit: Optimizing Client Selection for Robust Federated Learning Against Poisoning Attacks. [PUB]
• Cascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection. [PUB]
• Efficient Adapting for Vision-language Foundation Model in Edge Computing Based on Personalized and Multi-Granularity Federated Learning. [PUB]
• Distributed Link Heterogeneity Exploitation for Attention-Weighted Robust Federated Learning in 6G Networks. [PUB]
• GAN-Based Privacy Abuse Attack on Federated Learning in IoT Networks. [PUB]
• Fedkit: Enabling Cross-Platform Federated Learning for Android and iOS. [PUB] [CODE]
• ASR-FED: Agnostic Straggler Resilient Federated Algorithm for Drone Networks Security. [PUB]
• Unbiased Federated Learning for Heterogeneous Data Under Unreliable Links. [PUB]
• Efficient Client Sampling with Compression in Heterogeneous Federated Learning. [PUB]
• Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach. [PUB]
• Two-Timescale Energy Optimization for Wireless Federated Learning. [PUB]
• A Data Reconstruction Attack Against Vertical Federated Learning Based on Knowledge Transfer. [PUB]
• Federated Learning for Energy-efficient Cooperative Perception in Connected and Autonomous Vehicles. [PUB]
• Federated Learning-Based Cooperative Model Training for Task-Oriented Semantic Communication. [PUB]
• FedBF16-Dynamic: Communication-Efficient Federated Learning with Adaptive Transmission. [PUB]
• Designing Robust 6G Networks with Bimodal Distribution for Decentralized Federated Learning. [PUB]
• Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching. [PUB]
• Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning. [PUB]
• Joint Client Selection and Privacy Compensation for Differentially Private Federated Learning. [PUB]
• Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity. [PUB]

MobiCom

• Federated Black-box Prompt Tuning System for Large Language Models on the Edge. [PUB]
• Proximal Federated Learning for Body Mass Index Monitoring using Commodity WiFi. [PUB]
• ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues. [PUB]
• Joint Horizontal and Vertical Federated Learning for Multimodal IoT. [PUB]
• LATTE: Layer Algorithm-aware Training Time Estimation for Heterogeneous Federated Learning. [PUB]
• A Client Detection and Parameter Correction Algorithm for Clustering Defense in Clustered Federated Learning. [PUB]
• Exploring Visual Explanations for Defending Federated Learning against Poisoning Attacks. [PUB]
• ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease. [PUB] [PDF] [CODE]

WWW

• Accelerating the Decentralized Federated Learning via Manipulating Edges. [PUB]
• Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation. [PUB] [PDF] [CODE]
• PAGE: Equilibrate Personalization and Generalization in Federated Learning. [PUB] [PDF] [CODE]
• Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models. [PUB]
• Co-clustering for Federated Recommender System. [PUB]
• Incentive and Dynamic Client Selection for Federated Unlearning. [PUB]
• Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training. [PUB] [PDF] [CODE]
• BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework. [PUB] [PDF]
• Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation. [PUB] [PDF]
• FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices. [PUB]
• Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach. [PUB]
• Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation. [PUB] [PDF]
• Poisoning Federated Recommender Systems with Fake Users. [PUB] [PDF]
• Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs. [PUB]
• Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience. [PUB] [CODE]
• When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions. [PUB] [PDF] [CODE]
• How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments. [PUB] [CODE] [VIDEO]
• Poisoning Attack on Federated Knowledge Graph Embedding. [PUB] [CODE]

WWW (Companion Volume)

• FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web. [PUB] [PAGE]
• An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers. [PUB]
• Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning. [PUB]
• Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation. [PUB] [PDF]
• FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling. [PUB]
• HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models. [PUB]
• Phoenix: A Federated Generative Diffusion Model. [PUB]
• Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management. [PUB]
• Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks. [PUB] [PDF]
• GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning. [PUB]
• Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping. [PUB]

2023

MobiCom

• AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving. [PUB] [PDF]
• Efficient Federated Learning for Modern NLP. [PDF] [解读]

NSDI

• FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning. [PUB] [SLIDE] [VIDEO]

WWW

• To Store or Not? Online Data Selection for Federated Learning with Limited Storage. [PUB] [PDF]
• pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning. [PUB]
• Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding. [PUB] [PDF]
• Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks. [PUB] [PDF] [CODE]
• Semi-decentralized Federated Ego Graph Learning for Recommendation. [PUB] [PDF]
• FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures. [PUB] [CODE]
• FedEdge: Accelerating Edge-Assisted Federated Learning. [PUB]
• Federated Node Classification over Graphs with Latent Link-type Heterogeneity. [PUB] [CODE]
• FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. [PUB] [PDF] [CODE]
• Interaction-level Membership Inference Attack Against Federated Recommender Systems. [PUB] [PDF]
• AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning. [PUB]
• Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning. [PUB] [PDF] [CODE]

WWW (Companion Volume)

• Federated Learning for Metaverse: A Survey. [PUB] [PDF]
• Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification. [PUB]
• Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. [PUB] [PDF]
• Privacy-Preserving Online Content Moderation with Federated Learning. [PUB]
• A Federated Learning Benchmark for Drug-Target Interaction. [PUB] [PDF] [CODE]
• Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces. [PUB]
• 1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies. [PUB]
• A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy. [PUB] [PDF]

INFOCOM

• A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning. [PUB]
• A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning. [PUB]
• Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning. [PUB] [PDF]
• AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices. [PUB] [PDF]
• AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation. [PUB]
• Asynchronous Federated Unlearning. [PUB] [PDF] [CODE]
• Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization. [PUB] [PDF]
• Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing. [PUB]
• Federated Learning under Heterogeneous and Correlated Client Availability. [PUB] [PDF] [CODE]
• Federated Learning with Flexible Control. [PUB] [PDF] [CODE]
• Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks. [PUB] [PDF]
• FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection. [PUB] [PDF] [CODE]
• FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning. [PUB] [PDF]
• Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression.
• Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning. [PUB]
• Joint Participation Incentive and Network Pricing Design for Federated Learning. [PUB]
• More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning. [PUB] [PDF] [WEIBO]
• Network Adaptive Federated Learning: Congestion and Lossy Compression. [PUB] [PDF]
• OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting. [PUB] [CODE]
• Privacy as a Resource in Differentially Private Federated Learning. [PUB]
• SplitGP: Achieving Both Generalization and Personalization in Federated Learning. [PUB] [PDF]
• SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition. [PUB]
• Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis. [PUB] [PDF]
• Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions. [PUB] [PDF]
• Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling. [PUB] [PDF]
• TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving. [PUB]

2022

MobiCom

• PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning. [PUB] [PDF] [CODE]

MobiCom(Poster)

• NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing. [PUB]
• Federated learning-based air quality prediction for smart cities using BGRU model. [PUB]

MobiCom(Demo)

• FedHD: federated learning with hyperdimensional computing. [PUB] [CODE]

INFOCOM

• Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks. [PUB]
• Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending. [PUB]
• Optimal Rate Adaption in Federated Learning with Compressed Communications. [PUB] [PDF]
• The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. [PUB] [PDF]
• Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling. [PUB] [PDF]
• Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization. [PUB] [PDF]
• FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors. [PUB] [CODE]
• A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning. [PUB]
• Protect Privacy from Gradient Leakage Attack in Federated Learning. [PUB] [SLIDE]
• FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining. [PUB] [CODE]

WWW

• An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning. [PUB] [PDF] [CODE]
• LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning. [PUB]
• Federated Unlearning via Class-Discriminative Pruning. [PUB] [PDF] [CODE]
• FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding. [PUB]

WWW (Companion Volume)

• Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing.

2021

SIGMETRICS

• Federated Bandit: A Gossiping Approach. [PUB] [PDF]

MobiCom

• Hermes: an efficient federated learning framework for heterogeneous mobile clients. [PUB]
• Federated mobile sensing for activity recognition. [PUB] [PAGE] [TALKS] [VIDEO]

INFOCOM

• Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning. [PUB]
• Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation. [PUB] [PDF]
• FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation. [PUB]
• Sample-level Data Selection for Federated Learning. [PUB]
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices. [PUB] [PDF]
• Cost-Effective Federated Learning Design. [PUB] [PDF]
• An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective. [PUB]
• Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing. [PUB]
• FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism. [PUB] [PDF]
• Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach. [PUB] [PDF]
• Dual Attention-Based Federated Learning for Wireless Traffic Prediction. [PUB] [PDF] [CODE]
• FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing. [PUB]
• P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees. [PUB]

WWW

• Meta-HAR: Federated Representation Learning for Human Activity Recognition. [PUB] [PDF] [CODE]
• PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization. [PUB] [PDF] [CODE]
• Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics. [PUB] [CODE]
• Hierarchical Personalized Federated Learning for User Modeling. [PUB]
• Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data. [PUB] [PDF] [SLIDE] [CODE]
• Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction. [PUB]

2020

INFOCOM

• Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks. [PUB]
• Optimizing Federated Learning on Non-IID Data with Reinforcement Learning . [PUB] [SLIDE] [CODE] [解读]
• Enabling Execution Assurance of Federated Learning at Untrusted Participants. [PUB] [CODE]

MobiCom

• Billion-scale federated learning on mobile clients: a submodel design with tunable privacy. [PUB]

2019

INFOCOM

• Federated Learning over Wireless Networks: Optimization Model Design and Analysis. [PUB] [CODE]
• Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. [PUB] [PDF] [UC.]

2018

INFOCOM

• InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy. [PUB]

fl in top system conference and journal

Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), EuroSys(European Conference on Computer Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems), DAC(Design Automation Conference), TOCS(ACM Transactions on Computer Systems), TOS(ACM Transactions on Storage), TCAD(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), TC(IEEE Transactions on Computers).

• OSDI 2021
• SOSP 2021
• ISCA 2024
• MLSys 2024, 2023, 2022, 2020, 2019
• EuroSys 2025, 2024, 2023, 2022, 2021, 2020
• TPDS 2025, 2024, 2023, 2022, 2021, 2020
• DAC 2025, 2024, 2022, 2021
• TOCS NULL
• TOS NULL
• TCAD 2025, 2024, 2023, 2022, 2021
• TC 2025, 2024, 2023, 2022, 2021

fl in top system conference and journal

2025

DAC

• PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints. [PUB]
• MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems. [PUB]
• Resilient Federated Learning on Embedded Devices with Constrained Network Connectivity. [PUB]
• FedEDA: Federated Learning Framework for Privacy-Preserving Machine Learning in EDA. [PUB]

TCAD

• HaloFL: Efficient Heterogeneity-Aware Federated Learning Through Optimal Submodel Extraction and Dynamic Sparse Adjustment.
• Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators. [PUB]
• pFed-Litho: Lithography Modeling With a Personalized Federated Learning-Based Framework. [PUB]
• SAFE: A Scalable Homomorphic Encryption Accelerator for Vertical Federated Learning. [PUB]

EuroSys

• Hourglass: Enabling Efficient Split Federated Learning with Data Parallelism. [PUB]

TPDS

• Decentralized QoS-Aware Model Inference Using Federated Split Learning for Cloud-Edge Medical Detection. [PUB]
• Boosting Resource-Constrained Federated Learning Systems With Guessed Updates. [PUB]
• Coordinating Computational Capacity for Adaptive Federated Learning in Heterogeneous Edge Computing Systems. [PUB]
• FedCSpc: A Cross-Silo Federated Learning System With Error-Bounded Lossy Parameter Compression. [PUB]
• An Efficient Speculative Federated Tree Learning System With a Lightweight NN-Based Predictor. [PUB]
• FedLoRE: Communication-Efficient and Personalized Edge Intelligence Framework via Federated Low-Rank Estimation. [PUB]
• DegaFL: Decentralized Gradient Aggregation for Cross-Silo Federated Learning. [PUB]

TC

• BHerd: Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients. [PUB]
• Fed-OGD: Mitigating Straggler Effects in Federated Learning via Orthogonal Gradient Descent. [PUB]
• Asymmetrically Decentralized Federated Learning. [PUB]
• Adaptive Incentivize for Federated Learning With Cloud-Edge Collaboration Under Multi-Level Information Sharing. [PUB]
• SAFA: Handling Sparse and Scarce Data in Federated Learning With Accumulative Learning. [PUB]
• PFed-NS: An Adaptive Personalized Federated Learning Scheme Through Neural Network Segmentation. [PUB]
• Pruning-Based Adaptive Federated Learning at the Edge. [PUB]
• Federated Learning Based DDoS Attacks Detection in Large Scale Software-Defined Network. [PUB]
• Collaborative Neural Architecture Search for Personalized Federated Learning. [PUB]
• FedQClip: Accelerating Federated Learning via Quantized Clipped SGD. [PUB]
• Balancing Privacy and Accuracy Using Significant Gradient Protection in Federated Learning. [PUB]
• Sketch-Based Adaptive Communication Optimization in Federated Learning. [PUB]
• Towards Optimal Customized Architecture for Heterogeneous Federated Learning With Contrastive Cloud-Edge Model Decoupling. [PUB] [CODE]

2024

DAC

• AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. [PUB]
• Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks. [PUB]

ISCA

• Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation. [PUB]

MLSys

• FedTrans: Efficient Federated Learning via Multi-Model Transformation. [PUB] [PDF]
• LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning. [PUB] [PDF]
• HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning. [PUB] [PDF] [CODE]

EuroSys

• DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation. [PUB]
• FLOAT: Federated Learning Optimizations with Automated Tuning. [PUB] [CODE]
• Totoro: A Scalable Federated Learning Engine for the Edge. [PUB]
• Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy. [PUB] [PDF] [CODE]

EuroSys workshop

• FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN. [PUB]
• ALS Algorithm for Robust and Communication-Efficient Federated Learning. [PUB]
• FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. [PUB]

TPDS

• Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting. [PUB]
• SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning. [PUB]
• FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective. [PUB]
• Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning. [PUB]
• Accelerating Communication-Efficient Federated Multi-Task Learning With Personalization and Fairness. [PUB]
• Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning. [PUB]
• High-Performance Hardware Acceleration Architecture for Cross-Silo Federated Learning. [PUB]
• Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds. [PUB]
• Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively. [PUB]
• FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability. [PUB]
• Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning. [PUB] [PDF]
• Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection. [PUB]
• FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing. [PUB] [PDF]
• Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis. [PUB]
• FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training. [PUB]
• EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning. [PUB] [PDF] [CODE]
• FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval. [PUB] [PDF]

TCAD

• FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios. [PUB]
• Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring. [PUB]
• NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems. [PUB]
• CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance. [PUB]
• FedStar: Efficient Federated Learning on Heterogeneous Communication Networks. [PUB]
• Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection. [PUB] [PDF]
• FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices. [PUB]

TC

• BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework. [PUB]
• User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference. [PUB]
• FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality. [PUB] [PDF]
• Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning. [PUB]
• Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing. [PUB]
• FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation. [PUB] [PDF]
• Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks. [PUB]

2023

EuroSys

• REFL: Resource-Efficient Federated Learning. [PUB] [PDF] [CODE]

EuroSys workshop

• A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation. [PUB]
• Towards Practical Few-shot Federated NLP. [PUB]
• Can Fair Federated Learning Reduce the need for Personalisation?. [PUB]
• Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates. [PUB]
• Towards Robust and Bias-free Federated Learning. [PUB]

MLSys

• FedTree: A Federated Learning System For Trees. [PUB] [CODE]
• FLINT: A Platform for Federated Learning Integration. [PUB] [PDF]
• On Noisy Evaluation in Federated Hyperparameter Tuning. [PUB] [PDF] [CODE]
• GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning. [PUB] [PDF] [CODE]

TCAD

• Self-Supervised On-Device Federated Learning From Unlabeled Streams. [PUB] [PDF]
• Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing. [PUB]

TC

• Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning. [PUB]
• Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning. [PUB]
• A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks. [PUB]
• Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge. [PUB]
• Byzantine-Resilient Federated Learning at Edge. [PUB] [PDF]
• PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning. [PUB]
• Accelerating Federated Learning With a Global Biased Optimiser. [PUB] [PDF] [CODE]
• Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms. [PUB] [CODE]
• Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. [PUB]

TPDS

• CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks. [PUB]
• Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks. [PUB] [PDF]
• Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation. [PUB] [PDF] [CODE]
• Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach. [PUB]
• Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing. [PUB]
• Faster Federated Learning With Decaying Number of Local SGD Steps. [PUB] [PDF] [CODE]
• DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images. [PUB]
• FedProf: Selective Federated Learning Based on Distributional Representation Profiling. [PUB] [PDF] [UC]
• Federated Ensemble Model-Based Reinforcement Learning in Edge Computing. [PUB] [PDF]
• Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning. [PUB] [PDF]
• HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association. [PUB] [PDF]
• From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization. [PUB] [PDF] [CODE]
• Federated Learning Over Coupled Graphs. [PUB] [PDF]
• Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning. [PUB]
• On Model Transmission Strategies in Federated Learning With Lossy Communications. [PUB]
• Scheduling Algorithms for Federated Learning With Minimal Energy Consumption. [PUB] [PDF] [CODE]
• Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems. [PUB] [PDF]
• Personalized Edge Intelligence via Federated Self-Knowledge Distillation. [PUB] [CODE]
• Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning. [PUB]
• Multi-Job Intelligent Scheduling With Cross-Device Federated Learning. [PUB] [PDF]
• Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks. [PUB]
• GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. [PUB]
• FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework. [PUB]
• HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing. [PUB]

2022

EuroSys workshop

• Data selection for efficient model update in federated learning. [PUB]
• Empirical analysis of federated learning in heterogeneous environments. [PUB]

TC

• BAFL: A Blockchain-Based Asynchronous Federated Learning Framework. [PUB] [CODE]
• L4L: Experience-Driven Computational Resource Control in Federated Learning. [PUB]
• Adaptive Federated Learning on Non-IID Data With Resource Constraint. [PUB]

TCAD

• Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling. [PUB]
• Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning. [PUB]
• PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems. [PUB]

DAC

• FHDnn: communication efficient and robust federated learning for AIoT networks. [PUB]

TPDS

• A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee. [PUB] [PDF]
• Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation. [PUB]
• $f$funcX: Federated Function as a Service for Science. [PUB] [PDF]
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation. [PUB] [PDF] [CODE]
• Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing. [PUB]
• TDFL: Truth Discovery Based Byzantine Robust Federated Learning. [PUB]
• Federated Learning With Nesterov Accelerated Gradient. [PUB] [PDF]
• FedGraph: Federated Graph Learning with Intelligent Sampling. [PUB] [CODE] [解读]
• AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning. [PUB]
• DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. [PUB] [PDF] [CODE]
• Flexible Clustered Federated Learning for Client-Level Data Distribution Shift. [PUB] [PDF] [CODE]
• Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks. [PUB]
• LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. [PUB]
• On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. [PUB] [PDF] [CODE]
• Incentive-Aware Autonomous Client Participation in Federated Learning. [PUB]
• Communicational and Computational Efficient Federated Domain Adaptation. [PUB]
• Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning. [PUB]
• Differentially Private Byzantine-Robust Federated Learning. [PUB]
• Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing. [PUB] [PDF] [CODE]
• Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning. [PUB]
• Differentially Private Federated Temporal Difference Learning. [PUB]
• Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data. [PUB] [PDF]
• Communication-Efficient Federated Learning With Compensated Overlap-FedAvg. [PUB] [PDF] [CODE]

MLSys

• PAPAYA: Practical, Private, and Scalable Federated Learning. [PUB] [PDF]
• LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning. [PUB] [PDF] [CODE]

2021

EuroSys workshop

• Accelerated Training via Device Similarity in Federated Learning. [PUB]
• Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients. [PUB]
• Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. [PUB]

TC

• SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead. [PDF] [PUB] [CODE]

TCAD

• Efficient Federated Learning for Cloud-Based AIoT Applications. [PUB]

DAC

• HADFL: Heterogeneity-aware Decentralized Federated Learning Framework. [PDF] [PUB]
• Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. [PDF] [PUB]
• FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. [PUB]

OSDI

• Oort: Efficient Federated Learning via Guided Participant Selection. [PUB] [PDF] [CODE] [SLIDES] [VIDEO]

TPDS

• Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity. [PUB] [PDF]
• Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems. [PUB] [CODE]
• An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee. [PUB] [PDF] [解读]
• Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm. [PUB] [PDF]
• Biscotti: A Blockchain System for Private and Secure Federated Learning. [PUB]
• Mutual Information Driven Federated Learning. [PUB]
• Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems. [PUB] [PDF]
• FedSCR: Structure-Based Communication Reduction for Federated Learning. [PUB]

SOSP workshop / ICML 2022

• FedScale: Benchmarking Model and System Performance of Federated Learning . [PUB] [PDF] [CODE] [解读]

SOSP workshop

• Redundancy in cost functions for Byzantine fault-tolerant federated learning. [PUB]
• Towards an Efficient System for Differentially-private, Cross-device Federated Learning. [PUB]
• GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks. [PUB]
• Community-Structured Decentralized Learning for Resilient EI. [PUB]
• Separation of Powers in Federated Learning (Poster Paper). [PUB] [PDF]

2020

EuroSys workshop

• Towards federated unsupervised representation learning. [PUB]
• CoLearn: enabling federated learning in MUD-compliant IoT edge networks. [PUB]
• LDP-Fed: federated learning with local differential privacy. [PUB]

TPDS

• Accelerating Federated Learning via Momentum Gradient Descent. [PUB] [PDF]
• Towards Fair and Privacy-Preserving Federated Deep Models. [PUB] [PDF] [CODE]

MLSys

• Federated Optimization in Heterogeneous Networks . [PUB] [PDF] [CODE]

2019

MLSys

• Towards Federated Learning at Scale: System Design. [PUB] [PDF] [解读]

fl in top conference and journal other fields

Federated Learning papers accepted by top conference and journal in the other fields, including ICSE(International Conference on Software Engineering), FOCS(IEEE Annual Symposium on Foundations of Computer Science), STOC(Symposium on the Theory of Computing).

• ICSE 2025, 2023, 2021
• FOCS Null
• STOC Null

fl in top conference and journal other fields

2025

ICSE

• TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance. PUB PDF

SVM@ICSE

• Edge-Based Detection of Label Flipping Attacks in Federated Learning Using Explainable AI. PUB

SEiGS@ICSE

• Towards an Adaptive and Federated Testbed for AI Research in Africa. PUB

2024

ICSE Companion

• F-CodeLLM: A Federated Learning Framework for Adapting Large Language Models to Practical Software Development. PUB

SEAMS@ICSE

• Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning. PUB

2023

ICSE

• FedDebug: Systematic Debugging for Federated Learning Applications. pub pdf code
• FedSlice: Protecting Federated Learning Models from Malicious Participants with Model Slicing. pub code

2021

SEAMS@ICSE workshop

• Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems. pub
• Federated Machine Learning as a Self-Adaptive Problem. pub

fl on graph data and graph neural networks

This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.

fl on graph data and graph neural networks

Title	Venue	Year	Materials
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks	NeurIPS	2023	[PDF] [CODE]
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking	NeurIPS Dataset Track	2023	[PDF] [DATASET] [CODE]
Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query.	IEEE Trans. Vis. Comput. Graph.	2023	[PUB] [PDF]
Personalized Subgraph Federated Learning	ICML	2023	[PDF]
Semi-decentralized Federated Ego Graph Learning for Recommendation	WWW	2023	[PUB] [PDF]
Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks	IEEE Trans. Inf. Forensics Secur.	2023	[PUB]
Federated Learning Over Coupled Graphs	IEEE Trans. Parallel Distributed Syst.	2023	[PUB] [PDF]
HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning	IEEE Trans. Vis. Comput. Graph.	2023	[PUB] [PDF]
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing	AAAI	2023	[PDF] [CODE]
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability	AAAI	2023	[PDF] [CODE]
An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning.	DASFAA	2023	[PUB]
GraphCS: Graph-based client selection for heterogeneity in federated learning	J. Parallel Distributed Comput.	2023	[PUB]
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach	IEEE Trans. Neural Networks Learn. Syst.	2023	[PUB] [PDF]
Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning	IEEE Trans. Intell. Transp. Syst.	2023	[PUB]
Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection.	IEEE J. Biomed. Health Informatics	2023	[PUB]
Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural	IEEE Trans. Ind. Informatics	2023	[PUB]
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning	IEEE Trans. Comput. Soc. Syst.	2023	[PUB] [PDF] [CODE]
ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.	Peer Peer Netw. Appl.	2023	[PUB]
FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning	IEEE Trans. Big Data	2023	[PUB]
Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges.	Expert Syst. Appl.	2023	[PUB]
FedGR: Federated Graph Neural Network for Recommendation System	Axioms	2023	[PUB]
S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling.	IEEE Trans. Green Commun. Netw.	2023	[PUB]
FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network	Appl. Soft Comput.	2023	[PUB]
GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network	ICOIN	2023	[PUB] [CODE]
Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks	IEEE Trans. Veh. Technol.	2023	[PUB]
FedRule: Federated Rule Recommendation System with Graph Neural Networks	IoTDI	2023	[PUB] [PDF] [CODE]
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	KDD	2022	[PUB] [PDF]
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning	KDD (Best Paper Award)	2022	[PDF] [CODE] [PUB]
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	ICML	2022	[PUB] [CODE]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg.	IJCAI	2022	[PUB] [PDF] [CODE]
Personalized Federated Learning With a Graph	IJCAI	2022	[PUB] [PDF] [CODE]
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	IJCAI	2022	[PUB] [PDF]
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	AAAI	2022	[PUB] [PDF] [CODE] [解读]
FedGraph: Federated Graph Learning with Intelligent Sampling	TPDS	2022	[PUB] [CODE] [解读]
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications surv.	SIGKDD Explor.	2022	[PUB] [PDF]
Semantic Vectorization: Text- and Graph-Based Models.	Federated Learning	2022	[PUB]
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs	ICDM	2022	[PUB] [PDF] [解读]
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks	ACSAC	2022	[PUB] [PDF]
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction	TMI	2022	[PUB] [PDF]
SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification.	PPSN	2022	[PUB]
Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network	WCSP	2022	[PUB]
A federated graph neural network framework for privacy-preserving personalization	Nature Communications	2022	[PUB] [CODE] [解读]
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning	INFOCOM Workshops	2022	[PUB]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg.	EMNLP	2022	[PUB] [PDF] [CODE]
Power Allocation for Wireless Federated Learning using Graph Neural Networks	ICASSP	2022	[PUB] [PDF] [CODE]
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization	ICASSP	2022	[PUB] [PDF] [CODE]
Graph-regularized federated learning with shareable side information	Knowl. Based Syst.	2022	[PUB]
Federated knowledge graph completion via embedding-contrastive learning kg.	Knowl. Based Syst.	2022	[PUB]
Federated Graph Learning with Periodic Neighbour Sampling	IWQoS	2022	[PUB]
FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation.	Big Data	2022	[PUB]
Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks.	IJCNN	2022	[PUB]
A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction.	ICTAI	2022	[PUB]
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy	KSEM	2022	[PUB] [PDF]
Clustered Graph Federated Personalized Learning.	IEEECONF	2022	[PUB]
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets.	MICCAI Workshop	2022	[PDF] [CODE]
Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs	Int. J. Bio Inspired Comput.	2022	[PUB]
Federated Multi-task Graph Learning	ACM Trans. Intell. Syst. Technol.	2022	[PUB]
Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning	WCNC	2022	[PUB]
Federated meta-learning for spatial-temporal prediction	Neural Comput. Appl.	2022	[PUB] [CODE]
BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning	IEEE Transactions on Big Data	2022	[PUB] [PDF]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	INFCOM Workshops	2022	[PUB]
Federated learning of molecular properties with graph neural networks in a heterogeneous setting	Patterns	2022	[PUB] [PDF] [CODE]
Graph Federated Learning for CIoT Devices in Smart Home Applications	IEEE Internet Things J.	2022	[PUB] [PDF] [CODE]
Multi-Level Federated Graph Learning and Self-Attention Based Personalized Wi-Fi Indoor Fingerprint Localization	IEEE Commun. Lett.	2022	[PUB]
Graph-Assisted Communication-Efficient Ensemble Federated Learning	EUSIPCO	2022	[PUB] [PDF]
Decentralized Graph Federated Multitask Learning for Streaming Data	CISS	2022	[PUB]
Neural graph collaborative filtering for privacy preservation based on federated transfer learning	Electron. Libr.	2022	[PUB]
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications	JBHI	2022	[PUB]
FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data	Mathematics	2022	[PUB]
Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance	ACM Trans. Intell. Syst. Technol.	2022	[PUB] [PDF] [解读]
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.	INFOCOM	2021	[PUB] [PDF]
Federated Graph Classification over Non-IID Graphs	NeurIPS	2021	[PUB] [PDF] [CODE] [解读]
Subgraph Federated Learning with Missing Neighbor Generation	NeurIPS	2021	[PUB] [PDF]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	KDD	2021	[PUB] [PDF] [CODE] [解读]
Differentially Private Federated Knowledge Graphs Embedding kg.	CIKM	2021	[PUB] [PDF] [CODE] [解读]
Decentralized Federated Graph Neural Networks	IJCAI Workshop	2021	[PDF]
FedSGC: Federated Simple Graph Convolution for Node Classification	IJCAI Workshop	2021	[PDF]
FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper	ICCAD	2021	[PUB]
FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting	IEEE Trans. Ind. Informatics	2021	[PUB]
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning	ICC	2021	[PUB] [PDF]
FedE: Embedding Knowledge Graphs in Federated Setting kg.	IJCKG	2021	[PUB] [PDF] [CODE]
Federated Knowledge Graph Embeddings with Heterogeneous Data kg.	CCKS	2021	[PUB]
A Graph Federated Architecture with Privacy Preserving Learning	SPAWC	2021	[PUB] [PDF] [解读]
Federated Social Recommendation with Graph Neural Network	ACM TIST	2021	[PUB] [PDF] [CODE]
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks surv.	ICLR Workshop / MLSys Workshop	2021	[PDF] [CODE] [解读]
A Federated Multigraph Integration Approach for Connectional Brain Template Learning	MICCAI Workshop	2021	[PUB] [CODE]
Cluster-driven Graph Federated Learning over Multiple Domains	CVPR Workshop	2021	[PDF] [解读]
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation	ICML workshop	2021	[PDF] [解读]
Decentralized federated learning of deep neural networks on non-iid data	ICML workshop	2021	[PDF] [CODE]
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling	IWQoS	2021	[PUB]
Federated Graph Neural Network for Cross-graph Node Classification	CCIS	2021	[PUB]
GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition	ICMLA	2021	[PUB]
Distributed Training of Graph Convolutional Networks	TSIPN	2021	[PUB] [PDF] [解读]
Decentralized federated learning for electronic health records	NeurIPS Workshop / CISS	2020	[PUB] [PDF] [解读]
ASFGNN: Automated Separated-Federated Graph Neural Network	PPNA	2020	[PUB] [PDF] [解读]
Decentralized federated learning via sgd over wireless d2d networks	SPAWC	2020	[PUB] [PDF]
SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure	BigData	2019	[PUB] [PDF]
Towards Federated Graph Learning for Collaborative Financial Crimes Detection	NeurIPS Workshop	2019	[PDF]
Federated learning of predictive models from federated Electronic Health Records	Int. J. Medical Informatics	2018	[PUB]
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks.	preprint	2023	[PDF] [CODE]
Graph-guided Personalization for Federated Recommendation.	preprint	2023	[PDF]
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery.	preprint	2023	[PDF]
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data	preprint	2023	[PDF]
Vertical Federated Graph Neural Network for Recommender System	preprint	2023	[PDF] [CODE]
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices	preprint	2023	[PDF]
Securing IoT Communication using Physical Sensor Data - Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning.	preprint	2023	[PDF]
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.	preprint	2023	[PDF]
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning	preprint	2023	[PDF]
Graph Federated Learning with Hidden Representation Sharing	preprint	2022	[PDF]
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction	preprint	2022	[PDF]
Federated Graph-based Networks with Shared Embedding	preprint	2022	[PDF]
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph	preprint	2022	[PDF]
Heterogeneous Federated Learning on a Graph.	preprint	2022	[PDF]
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs	preprint	2022	[PDF] [CODE]
Federated Graph Contrastive Learning	preprint	2022	[PDF]
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR	preprint	2022	[PDF]
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning	preprint	2022	[PDF]
Federated Graph Attention Network for Rumor Detection	preprint	2022	[PDF] [CODE]
FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning	preprint	2022	[PDF]
Privatized Graph Federated Learning	preprint	2022	[PDF]
Federated Graph Neural Networks: Overview, Techniques and Challenges surv.	preprint	2022	[PDF]
Decentralized event-triggered federated learning with heterogeneous communication thresholds.	preprint	2022	[PDF]
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks	preprint	2022	[PDF]
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks	preprint	2021	[PDF] [CODE]
PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method	preprint	2021	[PDF]
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries kg.	preprint	2021	[PDF]
Federated Myopic Community Detection with One-shot Communication	preprint	2021	[PDF]
Federated Graph Learning -- A Position Paper surv.	preprint	2021	[PDF]
A Vertical Federated Learning Framework for Graph Convolutional Network	preprint	2021	[PDF]
FedGL: Federated Graph Learning Framework with Global Self-Supervision	preprint	2021	[PDF]
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search	preprint	2021	[PDF]
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization	preprint	2021	[PDF] [CODE]
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty kg.	preprint	2020	[PDF]
GraphFederator: Federated Visual Analysis for Multi-party Graphs	preprint	2020	[PDF]
Privacy-Preserving Graph Neural Network for Node Classification	preprint	2020	[PDF]
Peer-to-peer federated learning on graphs	preprint	2019	[PDF] [解读]

Private Graph Neural Networks (todo)

• [Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. [PDF]
• [Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. [PDF]
• [Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. [PDF]
• [Arxiv 2020] Locally Private Graph Neural Networks. [PDF]

Private Graph Neural Networks (todo)

fl on tabular data

This section refers to DBLP search engine.

fl on tabular data

Title	Venue	Year	Materials
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework	IEEE Trans. Inf. Forensics Secur.	2023	[PUB] [CODE]
Incentive-boosted Federated Crowdsourcing	AAAI	2023	[PDF]
Explaining predictions and attacks in federated learning via random forests	Appl. Intell.	2023	[PUB] [CODE]
Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses	IEEE Trans. Ind. Informatics	2023	[PUB]
Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network.	Int. J. Neural Syst.	2023	[PUB]
FDPBoost: Federated differential privacy gradient boosting decision trees.	J. Inf. Secur. Appl.	2023	[PUB]
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates.	EuroMLSys	2023	[PUB] [PDF]
HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis	Entropy	2023	[PUB]
Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning	IEEE Access	2023	[PUB]
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	Proc. VLDB Endow.	2022	[PUB] [PDF] [CODE]
RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated Learning	IEEE Trans. Dependable Secur. Comput.	2022	[PUB] [PDF]
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	ICML	2022	[PUB] [PDF] [CODE]
Federated Boosted Decision Trees with Differential Privacy	CCS	2022	[PUB] [PDF] [CODE]
Federated Functional Gradient Boosting	AISTATS	2022	[PUB] [PDF] [CODE]
Tree-Based Models for Federated Learning Systems.	Federated Learning	2022	[PUB]
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case	ICCP	2022	[PUB]
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy	ISSRE	2022	[PDF]
Federated Random Forests can improve local performance of predictive models for various healthcare applications	Bioinform.	2022	[PUB] [CODE]
FLForest: Byzantine-robust Federated Learning through Isolated Forest	ICPADS	2022	[PUB]
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent.	IJCNN	2022	[PUB] [CODE]
Federated Forest	TBD	2022	[PUB] [PDF]
Sliding Focal Loss for Class Imbalance Classification in Federated XGBoost.	ISPA/BDCloud/SocialCom/SustainCom	2022	[PUB]
Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.	AMIA	2022	[PUB]
Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive load monitoring	e-Energy	2022	[PUB]
Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random Forest	IEEE Internet Things J.	2022	[PUB]
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems	IEEE Access	2022	[PUB] [PDF]
BOFRF: A Novel Boosting-Based Federated Random Forest Algorithm on Horizontally Partitioned Data	IEEE Access	2022	[PUB]
eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees	IEEE Access	2022	[PUB]
An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization	ACM Trans. Intell. Syst. Technol.	2022	[PUB] [PDF] [CODE]
An optional splitting extraction based gain-AUPRC balanced strategy in federated XGBoost for mitigating imbalanced credit card fraud detection	Int. J. Bio Inspired Comput.	2022	[PUB]
Random Forest Based on Federated Learning for Intrusion Detection	AIAI	2022	[PUB]
Cross-silo federated learning based decision trees	SAC	2022	[PUB]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	INFCOM Workshops	2022	[PUB]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	SIGMOD	2021	[PUB]
Boosting with Multiple Sources	NeurIPS	2021	[PUB]
SecureBoost: A Lossless Federated Learning Framework	IEEE Intell. Syst.	2021	[PUB] [PDF] [SLIDE] [CODE] [解读] [UC]
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System	IEEE Access	2021	[PUB]
Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics	Phys. Commun.	2021	[PUB]
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning	IEEE BigData	2021	[PUB] [PDF]
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs	ICONIP	2021	[PUB]
A k-Anonymised Federated Learning Framework with Decision Trees	DPM/CBT @ESORICS	2021	[PUB]
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests	SEAA	2021	[PUB]
Compression Boosts Differentially Private Federated Learning	EuroS&P	2021	[PUB] [PDF]
Practical Federated Gradient Boosting Decision Trees	AAAI	2020	[PUB] [PDF] [CODE]
Privacy Preserving Vertical Federated Learning for Tree-based Models	VLDB	2020	[PUB] [PDF] [VIDEO] [CODE]
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing	ICDCS	2020	[PUB] [PDF]
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling	IEEE BigData	2020	[PUB] [PDF]
New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis	ICONIP	2020	[PUB]
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks	IEEE Communications Letters	2020	[PUB]
DFedForest: Decentralized Federated Forest	Blockchain	2020	[PUB]
Straggler Remission for Federated Learning via Decentralized Redundant Cayley Tree	LATINCOM	2020	[PUB]
Federated Soft Gradient Boosting Machine for Streaming Data	Federated Learning	2020	[PUB] [解读]
Federated Learning of Deep Neural Decision Forests	LOD	2019	[PUB]
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables.	preprint	2023	[PDF]
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection.	preprint	2023	[PDF]
GTV: Generating Tabular Data via Vertical Federated Learning	preprint	2023	[PDF]
Federated Survival Forests	preprint	2023	[PDF]
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data	preprint	2022	[PDF]
Data Leakage in Tabular Federated Learning	preprint	2022	[PDF]
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources	preprint	2022	[PDF]
Federated XGBoost on Sample-Wise Non-IID Data	preprint	2022	[PDF]
Hercules: Boosting the Performance of Privacy-preserving Federated Learning	preprint	2022	[PDF]
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging	preprint	2022	[PDF]
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction.	preprint	2022	[PDF]
An Efficient and Robust System for Vertically Federated Random Forest	preprint	2022	[PDF]
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost.	preprint	2021	[PDF]
Guess what? You can boost Federated Learning for free	preprint	2021	[PDF]
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning	preprint	2021	[PDF] [CODE]
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data	preprint	2021	[PDF]
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning	preprint	2021	[PDF]
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning	preprint	2020	[PDF]
FederBoost: Private Federated Learning for GBDT	preprint	2020	[PDF]
Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning	preprint	2020	[PDF] [CODE]
Cloud-based Federated Boosting for Mobile Crowdsensing	preprint	2020	[ARXIV]
Federated Extra-Trees with Privacy Preserving	preprint	2020	[PDF]
Bandwidth Slicing to Boost Federated Learning in Edge Computing	preprint	2019	[PDF]
Revocable Federated Learning: A Benchmark of Federated Forest	preprint	2019	[PDF]
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost	preprint	2019	[PDF] [CODE]

framework

federated learning framework

table

Note: SG means Support for Graph data and algorithms, ST means Support for Tabular data and algorithms.

federated learning framework

Platform	Papers	Affiliations	SG	ST	Materials
PySyft	A generic framework for privacy preserving deep learning	OpenMined			[DOC]
Flower	Flower: A Friendly Federated Learning Research Framework	flower.ai			[DOC]
FATE	FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection	WeBank			[DOC] [DOC(ZH)]
FedML	FedML: A Research Library and Benchmark for Federated Machine Learning	FedML			[DOC]
TFF(Tensorflow-Federated)	Towards Federated Learning at Scale: System Design	Google			[DOC] [PAGE]
SecretFlow		Ant group			[DOC]
PFLlib	PFLlib: Personalized Federated Learning Algorithm Library	SJTU			[PAGE]
FederatedScope	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	Alibaba DAMO Academy			[DOC] [PAGE]
Primihub		primihub			[DOC]
NVFlare	NVIDIA FLARE: Federated Learning from Simulation to Real-World	NVIDIA			[DOC]
Fedlearner		Bytedance
LEAF	LEAF: A Benchmark for Federated Settings	CMU
OpenFL	OpenFL: An open-source framework for Federated Learning	Intel			[DOC]
Fedlab	FedLab: A Flexible Federated Learning Framework	SMILELab			[DOC] [DOC(ZH)] [PAGE]
Privacy Meter	Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	University of Massachusetts Amherst
NIID-Bench	Federated Learning on Non-IID Data Silos: An Experimental Study	Xtra Computing Group
FLGo	Federated Learning with Fair Averaging FLGo: A Fully Customizable Federated Learning Platform	XMU
Rosetta		matrixelements			[DOC] [PAGE]
IBM Federated Learning	IBM Federated Learning: an Enterprise Framework White Paper	IBM			[PAPERS]
PaddleFL		Baidu			[DOC]
KubeFATE		WeBank			[WIKI]
FedScale	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	SymbioticLab(U-M)
PersonalizedFL		microsoft
plato	Plato: An Open-Source Research Framework for Production Federated Learning	UofT
Differentially Private Federated Learning: A Client-level Perspective	Differentially Private Federated Learning: A Client Level Perspective	SAP-samples
Backdoors 101	Blind Backdoors in Deep Learning Models	Cornell Tech
SWARM LEARNING	Swarm Learning for decentralized and confidential clinical machine learning				[VIDEO]
EasyFL	EasyFL: A Low-code Federated Learning Platform For Dummies	NTU
Breaching	A Framework for Attacks against Privacy in Federated Learning (papers)
substra		Substra			[DOC]
FedJAX	FEDJAX: Federated learning simulation with JAX	Google
FLSim		facebook research
Galaxy Federated Learning	GFL: A Decentralized Federated Learning Framework Based On Blockchain	ZJU			[DOC]
FedNLP	FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks	FedML
PyVertical	PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN	OpenMined
FLSim	Optimizing Federated Learning on Non-IID Data with Reinforcement Learning	University of Toronto
Xaynet		XayNet			[PAGE] [DOC] [WHITEPAPER] [LEGAL REVIEW]
SyferText		OpenMined
FedTorch	Distributionally Robust Federated Averaging	Penn State
FLUTE	FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations	microsoft			[DOC]
FedGraphNN	FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks	FedML
FEDn	Scalable federated machine learning with FEDn	scaleoutsystems			[DOC]
FedTree	FedTree: A Federated Learning System For Trees	Xtra Computing Group			[DOC]
PhotoLabeller					[BLOG]
FATE-Serving		WeBank			[DOC]
PriMIA	End-to-end privacy preserving deep learning on multi-institutional medical imaging	TUM; Imperial College London; OpenMined			[DOC]
9nfl		JD
APPFL	APPFL: open-source software framework for privacy-preserving federated learning				[DOC]
FedLearn	Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	JD
FeTS	The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research	Federated Tumor Segmentation (FeTS) initiative			[DOC]
FedCV	FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks	FedML
MPLC		LabeliaLabs			[PAGE]
Flame	Flame: Simplifying Topology Extension in Federated Learning	Cisco			[DOC]
FlexCFL	Flexible Clustered Federated Learning for Client-Level Data Distribution Shift	Chongqing University
FedGroup	FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure	Chongqing University
FedEval	FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning	HKU			[DOC]
GOLF		SYSU			[DOC]
UCADI	Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence	Huazhong University of Science and Technology
OpenFed	OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework				[DOC]
FedSim
Federated-Learning-source	A Practical Federated Learning Framework for Small Number of Stakeholders	ETH Zürich			[DOC]
Clara		NVIDIA

benchmark

• UniFed leaderboard

Here's a really great Benchmark for the federated learning open source framework UniFed leaderboard, which present both qualitative and quantitative evaluation results of existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance.

For more results, please refer to Framework Functionality Support

datasets

fl graph datasets

• Wyze Rule Dataset

tabular datasets

fl datasets

• LEAF

• Federated AI Dataset

surveys

This section partially refers to repository Federated-Learning and FederatedAI research , the order of the surveys is arranged in reverse order according to the time of first submission (the latest being placed at the top)

• [Ad Hoc Networks 2024] Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges PUB PDF CODE
• [SIGKDD Explor. 2022] Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications PUB PDF
• [ACM Trans. Interact. Intell. Syst.] Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing PUB
• [ICML Workshop 2020] SECure: A Social and Environmental Certificate for AI Systems PDF
• [IEEE Commun. Mag. 2020] From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks PDF [PUB]
• [China Communications 2020] Federated Learning for 6G Communications: Challenges, Methods, and Future Directions PDF PUB
• [Federated Learning Systems] A Review of Privacy Preserving Federated Learning for Private IoT Analytics PDF [PUB]
• [WorldS4 2020] Survey of Personalization Techniques for Federated Learning PDF PUB
• Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective PDF
• [IEEE Internet Things J. 2022] A Survey on Federated Learning for Resource-Constrained IoT Devices PDF PUB
• [IEEE Communications Surveys & Tutorials 2020] Communication-Efficient Edge AI: Algorithms and Systems PDF PUB
• [IEEE Communications Surveys & Tutorials 2020] Federated Learning in Mobile Edge Networks: A Comprehensive Survey PDF PUB
• [IEEE Signal Process. Mag. 2020] Federated Learning: Challenges, Methods, and Future Directions PDF [PUB]
• [IEEE Commun. Mag. 2020] Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges PDF PUB
• [IEEE TKDE 2021] A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection PDF PUB
• [IJCAI Workshop 2020] Threats to Federated Learning: A Survey PDF
• [Foundations and Trends in Machine Learning 2021] Advances and Open Problems in Federated Learning PDF PUB
• Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing PDF
• An Introduction to Communication Efficient Edge Machine Learning PDF
• [IEEE Communications Surveys & Tutorials 2020] Convergence of Edge Computing and Deep Learning: A Comprehensive Survey PDF PUB
• [IEEE TIST 2019] Federated Machine Learning: Concept and Applications PDF [PUB]
• [J. Heal. Informatics Res. 2021] Federated Learning for Healthcare Informatics PDF PUB
• Federated Learning for Coalition Operations PDF
• No Peek: A Survey of private distributed deep learning PDF

tutorials and courses

tutorials

• 联邦学习入门教程参考

• [NeurIPS 2020] Federated Learning Tutorial [Web] [Slides] [Video]

• Federated Learning on MNIST using a CNN, AI6101, 2020 (Demo Video)

• [AAAI 2019] Federated Learning: User Privacy, Data Security and Confidentiality in Machine Learning

• Applied Cryptography

• A Brief Introduction to Differential Privacy

• Deep Learning with Differential Privacy.

• Building Safe A.I.

  • A Tutorial for Encrypted Deep Learning
  • Use Homomorphic Encryption (HE)

• Private Image Analysis with MPC

  • Training CNNs on Sensitive Data
  • Use SPDZ as MPC protocol

• Private Deep Learning with MPC

  • A Simple Tutorial from Scratch
  • Use Multiparty Compuation (MPC)

course

secret sharing

• Simple Introduction to Sharmir's Secret Sharing and Lagrange Interpolation
• Secret Sharing
  • Part 1: Shamir's Secret Sharing & Packed Variant
  • Part 2: Improve efficiency
  • Part 3: Robust Reconstruction

key conferences/workshops/journals

This section partially refers to The Federated Learning Portal.

workshops

• [FL@FM-IJCAI'24], International Workshop on Federated Learning in the Age of Foundation Models In Conjunction with IJCAI 2024, Jeju Island, South Korea
• [FL@FM-ICME'24],International Workshop on Federated Learning and Foundation Models for Multi-Media, Niagara Falls, ON, Canada
• [FL@FM-TheWebConf'24], International Workshop on Federated Foundation Models for the Web 2024 , Singapore
• [FL@FM-Singapore'24], Federated Learning in the Age of Foundation Models, Summer School @ Singapore 2024, Singapore
• [TTIC Chicago Summer Workshop] New Frontiers in Federated Learning (Recent Theoretical Advances & Practice), TTIC Chicago, USA
• [Federated and Collaborative Learning] Calvin Lab Auditorium
• [FL@FM-NeurIPS'23],International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023(FL@FM-NeurIPS’23), New Orleans, LA, USA
• [2nd MBZUAI Workshop] 2nd MBZUAI Workshop on Collaborative Learning: Empowering Sustainable Futures, Abu Dhabi, UAE
• [FL-CVPR'23], 2nd Workshop on Federated Learning for Computer Vision
• [FL@FM-AJCAI'23], Workshop on Federated Learning in Australasia: When FL meets Foundation Models in Conjunction with AJCAI 2023, Brisbane, Australia
• [FL-IJCAI'23], International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2023 (FL-IJCAI'23), Macau
• [FL-KDD'23], International Workshop on Federated Learning for Distributed Data Mining Co-located with the 29th ACM SIGKDD Conference (KDD 2023), Long Beach, CA, USA
• [FL-ICML'23],Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities Workshop at ICML 2023, Honolulu, HI, USA
• [FLIRT-SIGIR'23],1st Workshop on Federated Learning for Information ReTrieval, Taipei, Taiwan
• [FLSys'23], the Federated Learning Systems (FLSys) Workshop @ MLSys 2023, Miami, FL, USA
• [FLW@TheWebConf'23], 1st Workshop on Federated Learning Technologies, Austin, TX, USA
• [CIKM'22] The 1st International Workshop on Federated Learning with Graph Data (FedGraph), Atlanta, GA, USA
• [AI Technology School 2022] Trustable, Verifiable and Auditable Artificial Intelligence, Singapore
• [FL-CVPR'22] First International Workshop on Federated Learning for Computer Vision (FedVision)
• [FL-NeurIPS'22] International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 , New Orleans, LA, USA
• [FL-IJCAI'22] International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022, Vienna, Austria
• [FL-AAAI-22] International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022, Vancouver, BC, Canada (Virtual)
• [FL-MobiCom'22] FedEdge 2022, 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network -Research Track, Sydney, Australia
• [FL-NeurIPS'21] New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership, (Virtual)
• [The Federated Learning Workshop, 2021] , Paris, France (Hybrid)
• [PDFL-EMNLP'21] Workshop on Parallel, Distributed, and Federated Learning, Bilbao, Spain (Virtual)
• [FTL-IJCAI'21] International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021, Montreal, QB, Canada (Virtual)
• [DeepIPR-IJCAI'21] Toward Intellectual Property Protection on Deep Learning as a Services, Montreal, QB, Canada (Virtual)
• [FL-ICML'21] International Workshop on Federated Learning for User Privacy and Data Confidentiality, (Virtual)
• [RSEML-AAAI-21] Towards Robust, Secure and Efficient Machine Learning, (Virtual)
• [NeurIPS-SpicyFL'20] Workshop on Scalability, Privacy, and Security in Federated Learning, Vancouver, BC, Canada (Virtual)
• [FL-IJCAI'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Yokohama, Japan (Virtual)
• [FL-ICML'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Vienna, Austria (Virtual)
• [FL-IBM'20] Workshop on Federated Learning and Analytics, New York, NY, USA
• [FL-NeurIPS'19] Workshop on Federated Learning for Data Privacy and Confidentiality (in Conjunction with NeurIPS 2019), Vancouver, BC, Canada
• [FL-IJCAI'19] International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2019, Macau
• [FL-Google'19] Workshop on Federated Learning and Analytics, Seattle, WA, USA

journal special issues

• Special Issue on Trustworthy Federated Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
• Special Issue on Trustable, Verifiable, and Auditable Federated Learning, IEEE Transactions on Big Data (TBD), 2022.
• Special Issue on Federated Learning: Algorithms, Systems, and Applications, ACM Transactions on Intelligent Systems and Technology (TIST), 2021.
• Special Issue on Federated Machine Learning, IEEE Intelligent Systems (IS), 2019.

conference special tracks

• "Federated Learning" included as a new keyword in IJCAI'20, Yokohama, Japan
• Special Track on Federated Machine Learning, IEEE BigData'19, Los Angeles, CA, USA

update log

• 2026/04/13 - Simplify forms to reduce the burden of subsequent maintenance.
• 2025/10/05 - add IJCAI, AISTATS, UAI, ICDE, SIGCOMM, DAC 2025 papers
• 2025/08/03 - add S&P, CVPR, IJCV, ICML, ICSE, ACL, COLING, SIGIR, VLDB, INFOCOM 2025 papers
• 2025/05/31 - add AI, ML 2025 papers
• 2025/05/01 - add WWW, NSDI 2025 papers
• 2025/04/24 - add AAAI, KDD, ICLR 2025 papers
• 2025/04/04 - add UAI 2024, WSDM, NDSS, COLING, TPDS, ML 2025 papers
• 2024/12/30 - add CCS 2024 papers
• 2024/12/10 - add NeurIPS 2024 papers
• 2024/12/08 - add IJCV, IJCAI, MM, EMNLP, DAC 2024 papers
• 2024/12/05 - add TPDS, TCAD, ECCV 2024 papers
• 2024/09/30 - add JMLR, KDD, S&P, NDSS, NAACL, INFOCOM, ISCA, TC, ICML 2024 papers
• 2024/07/14 - add CVPR, MLSys, MobiCom, SIGIR 2024 papers
• 2024/05/20 - add WWW 2024 papers
• 2024/05/12 - add AISTATS, EuroSys 2024 papers
• 2024/04/15 - add TPAMI, VLDB 2024 papers
• 2024/04/10 - add WSDM, Mach Learn 2024 papers
• 2024/03/28 - add AAAI, TC, TPAMI, TPDS, VLDB 2024 papers
• 2024/03/08 - add TPAMI, TC, TCAD 2024 papers
• 2024/02/23 - add ICLR 2024 papers
• 2024/01/02 - add NeurIPS 2023 papers
• 2023/12/13 - add MM, CCS, EMNLP 2023 papers and update VLDB, TC, TCAD papers
• 2023/10/07 - add ICCV 2023 papers
• 2023/09/18 - add INFOCOM, S&P 2023 papers
• 2023/08/18 - add COLT 2023 paper
• 2023/08/16 - refresh KDD, IJCAI 2023 papers
• 2023/08/02 - refresh ICML 2023 information
• 2023/07/31 - add MobiCom, ICDE, EuroSys, USENIX Security 2023 papers
• 2023/07/24 - add SIGIR, UAI, ICSE 2023 papers and information on CVPR workshops
• 2023/07/03 - add Events for different conferences and journals like ACL
• 2023/07/01 - add AAAI, ICML, IJCAI, SIGIR, KDD, ACL 2023 papers
• 2023/06/28 - add AISTATS, MLsys, JMLR, Machine Learning, ALT, FOCS, STOC papers
• 2023/06/06 - remove 'tldr information' and change this repo from "Awesome-Federated-Learning-on-Graph-and-Tabular-Data" into "Awesome-FL".
• 2023/05/23 - add CVPR 2023 papers
• 2023/05/07 - add workshops and WWW 2023 papers
• 2023/04/02 - add NDSS 2023 papers and fix some typos
• 2023/02/19 - add INFOCOM 2023 papers
• 2023/02/14 - add EMNLP 2022 papers
• 2023/02/13 - add ICLR 2023 papers
• 2023/01/14 - add UAI 2022 papers, refresh system (TCAD +1, TPDS+8), ML (TPAMI +1,UAI +6), network(MobiCom +3) fields papers
• 2022/11/24 - refresh NeurIPS 2022,2021 and ICLR 2022 papers
• 2022/11/06- add S&P 2023 papers
• 2022/10/29 - add WSDM 2023 paper
• 2022/10/20 - add CCS, MM, ECCV 2022 papers
• 2022/10/16 - add AI, JMLR, TPAMI, IJCV, TOCS, TOS, TCAD, TC papers
• 2022/10/13 - add DAC papers
• 2022/10/09 - add MobiCom 2022 paper
• 2022/09/19 - add NeurIPS 2022 papers
• 2022/09/16 - repository is online with Github Pages
• 2022/09/06 - add information about FL on Tabular and Graph data
• 2022/09/05 - add some information about top journals and add TPDS papers
• 2022/08/31 - all papers (including 400+ papers from top conferences and top journals and 100+ papers with graph and tabular data) have been comprehensively sorted out, and information such as publication addresses, links to preprints and source codes of these papers have been compiled. The source code of 280+ papers has been obtained. We hope it can help those who use this project.
• 2022/07/31 - add VLDB papers
• 2022/07/30 - add top-tier system conferences papers and add COLT,UAI,OSDI, SOSP, ISCA, MLSys, AISTATS,WSDM papers
• 2022/07/28 - add a list of top-tier conferences papers and add IJCAI,SIGIR,SIGMOD,ICDE,WWW,SIGCOMM.INFOCOM,WWW papers
• 2022/07/27 - add some ECCV 2022 papers
• 2022/07/22 - add CVPR 2022 and MM 2020,2021 papers
• 2022/07/21 - give TL;DR and interpret information(解读) of papers. And add KDD 2022 papers
• 2022/07/15 - give a list of papers in the field of federated learning in top NLP/Secure conferences. And add ICML 2022 papers
• 2022/07/14 - give a list of papers in the field of federated learning in top ML/CV/AI/DM conferences from innovation-cat‘s Awesome-Federated-Machine-Learning and find papers(code is available & stars >= 100)
• 2022/07/12 - added information about the last commit time of the federated learning open source framework (can be used to determine the maintenance of the code base)
• 2022/07/12 - give a list of papers in the field of federated learning in top journals
• 2022/05/25 - complete the paper and code lists of FL on tabular data and Tree algorithms
• 2022/05/25 - add the paper list of FL on tabular data and Tree algorithms
• 2022/05/24 - complete the paper and code lists of FL on graph data and Graph Neural Networks
• 2022/05/23 - add the paper list of FL on graph data and Graph Neural Networks
• 2022/05/21 - update all of Federated Learning Framework

acknowledgments

Many thanks to the other awesome list:

• Federated Learning

  • Awesome-Federated-Learning-on-Graph-and-GNN-papers
  • Awesome-GNN-Research
  • Awesome-Federated-Machine-Learning
  • Awesome-Federated-Learning
  • awesome-federated-learning
  • Federated-Learning
  • FederatedAI research
  • FLsystem-paper
  • Federated Learning Framework Benchmark (UniFed)
  • awesome-privacy-chinese

• Other fields

  • anomaly-detection-resources
  • awesome-image-registration

citation

@misc{Awesome-FL,
    title = {Awesome-FL},
    author = {Yuwen Yang and Bingjie Yan and Xuefeng Jiang and Hongcheng Li and Jian Wang and Jiao Chen and Xiangmou Qu and Chang Liu and others},
    year = {2022},
    url = {https://github.com/youngfish42/Awesome-FL}
}

This site is open source. Improve this page.