Awesome-FL

Federated Learning Resources

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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

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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)

Venue	2024-2020	before 2020
IJCAI	25, 24, 23, 22, 21, 20	19
AAAI	26, 25, 24, 23, 22, 21, 20	-
AISTATS	25, 24, 23, 22, 21, 20	-
ALT	22	-
AI (J)	26, 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)	26, 25, 24, 23, 22	-
JMLR (J)	25, 24, 23, 22	-
TPAMI (J)	26, 25, 24, 23, 22	-
KDD	26, 25, 24, 23, 22, 21, 20
WSDM	26,25, 24, 23, 22, 21	19
S&P	25, 24, 23, 22	19
CCS	25, 24, 23, 22, 21, 19	17
USENIX Security	25, 24, 23, 22, 20	-
NDSS	26, 25, 24, 23, 22, 21	-
CVPR	25, 24, 23, 22, 21	-
ICCV	23,21	-
ECCV	24, 22, 20	-
MM	25, 24, 23, 22, 21, 20	-
IJCV (J)	25, 24	-
ACL	25, 24, 23, 22, 21	19
NAACL	24, 22, 21	-
EMNLP	25, 24, 23, 22, 21, 20	-
COLING	25, 20	-
SIGIR	25, 24, 23, 22, 21, 20	-
SIGMOD	25, 24, 23, 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	25, 24, 23, 22, 21, 20
NSDI	25, 23(1, 2)	-
WWW	26, 25, 24, 23, 22, 21
OSDI	21	-
SOSP	21	-
ISCA	24	-
MLSys	25, 24, 23, 22, 20	19
EuroSys	26, 25, 24, 23, 22, 21, 20
TPDS (J)	26, 25, 24, 23, 22, 21, 20	-
DAC	25, 24, 22, 21	-
TOCS	-	-
TOS	-	-
TCAD	26, 25, 24, 23, 22, 21	-
TC	26, 25, 24, 23, 22, 21	-
ICSE	25, 23, 21	-
FOCS	-	-
STOC	-	-

keywords

Statistics: :fire: code is available & stars >= 100 | :star: citation >= 50 | :mortar_board: 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.

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 :star:	Nature :mortar_board:	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 :star:	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 2026, 2025, 2024, 2023, 2022, 2021, 2020
• AISTATS 2025, 2024, 2023, 2022, 2021, 2020
• ALT 2022
• AI 2026, 2025, 2023

2026

AAAI

• A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning. [PUB]
• Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach. [PUB]
• BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning. [PUB]
• Breaking Cross-View Associations: Byzantine Model Poisoning Attack against Vertical Federated Learning. [PUB]
• Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. [PUB]
• Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs. [PUB]
• Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering. [PUB]
• Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification. [PUB]
• Communication-Efficient Heterogeneous Federated Learning with Sparse Prototypes in Resource-Constrained Environments. [PUB]
• CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation. [PUB]
• DA-DFGAS: Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation. [PUB]
• Data Heterogeneity and Forgotten Labels in Split Federated Learning. [PUB]
• Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data. [PUB]
• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation. [PUB]
• DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew. [PUB]
• Domain-Aware Suppression and Aggregation for Federated DG ReID. [PUB]
• DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models. [PUB]
• Enhanced Federated Deep Multi-View Clustering Under Uncertainty Scenario. [PUB]
• Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection. [PUB]
• EvoFMVC: Trusted Federated Multi-View Clustering with Evolutionary Fusion. [PUB]
• Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences. [PUB]
• FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. [PUB]
• FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA. [PUB]
• FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification. [PUB]
• FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training. [PUB]
• FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning. [PUB]
• FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning. [PUB]
• FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. [PUB]
• FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation. [PUB]
• Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification. [PUB]
• Federated Context-Aware Personalized Recommendation. [PUB]
• Federated Graph-level Clustering Network with Attribute Inference. [PUB]
• Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint. [PUB]
• Federated Learning Playground. [PUB]
• Federated Linear Dueling Bandits. [PUB]
• Federated Vision-Language-Recommendation with Personalized Fusion. [PUB]
• FedLAGC: Towards High Performance System-Heterogeneous Federated Learning via Layer-Adaptive Submodel Extraction and Gradient Correction. [PUB]
• FedMerge: Federated Model Merging for Personalization. [PUB]
• FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment. [PUB]
• FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. [PUB]
• FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs. [PUB]
• FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning. [PUB]
• FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification. [PUB]
• FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD. [PUB]
• FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models. [PUB]
• FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness. [PUB]
• FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting. [PUB]
• FedTopo: Topology-Informed Representation Alignment in Federated Learning Under Non-I.I.D. Conditions. [PUB]
• FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning. [PUB]
• Generalizable Heterogeneity-aware Federated Feature and Basic-matrix Consistency Learning. [PUB]
• Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning. [PUB]
• Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation. [PUB]
• HealSplit: Towards Self-Healing Through Adversarial Distillation in Split Federated Learning. [PUB]
• Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. [PUB]
• Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. [PUB]
• Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction. [PUB]
• Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning. [PUB]
• Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models. [PUB]
• LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping. [PUB]
• MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift. [PUB]
• Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization. [PUB]
• MultiKD: Backdoor Defense in Federated Graph Learning via Attention-Guided Multi-Teacher Distillation. [PUB]
• Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling. [PUB]
• Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models. [PUB]
• Optimal Look-back Horizon for Time Series Forecasting in Federated Learning. [PUB]
• OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks. [PUB]
• OursFed: Provable Group Fairness-Aware Federated Learning Against Distrust and Fragility. [PUB]
• PAGE: A Unified Approach for Federated Graph Unlearning. [PUB]
• Personalized Federated Graph-Level Clustering Network. [PUB]
• Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching. [PUB]
• Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation. [PUB]
• Poisoning with a Pill: Circumventing Detection in Federated Learning. [PUB]
• PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning. [PUB]
• Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning. [PUB]
• Re-architecting Personalized Federated Learning for Demanding Edge Environments. [PUB]
• REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature. [PUB]
• Retaliatory Attacks Against Federated Unlearning via Data Leakage. [PUB]
• Ripple Shapley: Data Influence Attribution in One Federated Training Run. [PUB]
• Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?. [PUB]
• SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning. [PUB]
• ShadeEdit: A Utility-Preserving and Defense-Evasive Knowledge Manipulation Attack in Federated LLMs. [PUB]
• SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data. [PUB]
• Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack. [PUB]
• TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models. [PUB]
• Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy. [PUB]
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework. [PUB]
• Towards Robust Text-Attributed Federated Graph Learning: Multimodal Threats and Defense. [PUB]
• TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models. [PUB]
• Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting. [PUB]
• Venom: Liquid Diffusion-Guided Gradient Inversion for Breaking Differential Privacy in Federated Learning. [PUB]
• AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract). [PUB]
• Federated Cross-Modal Style-Aware Prompt Generation (Student Abstract). [PUB]
• UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data (Student Abstract). [PUB]
• A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning. [PUB]
• Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization. [PUB]
• CL-Guard: Defending DNNs Against Backdoors via Fine-Grained Neuron Analysis and Collaborative Dual-Network Learning. [PUB]
• Collaborative Dual Representations for Semi-Supervised Partial Label Learning. [PUB]
• Collaborative Feature Matching with Progressive Correspondence Learning. [PUB]
• Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities. [PUB]
• Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models. [PUB]
• DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning. [PUB]
• Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging. [PUB]
• Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning. [PUB]
• From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging. [PUB]
• GLOBA: Rethinking Parameter Conflicts in Model Merging. [PUB]
• Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation. [PUB]
• Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learning. [PUB]
• Learning to Generate and Extract: A Multi-Agent Collaboration Framework for Zero-Shot Document-Level Event Arguments Extraction. [PUB]
• LLM Collaboration with Multi-Agent Reinforcement Learning. [PUB]
• M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data. [PUB]
• MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation. [PUB]
• MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging. [PUB]
• Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization. [PUB]
• Outlier Matters: Efficient Long-to-Short Reasoning via Outlier-Guided Model Merging. [PUB]
• RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior. [PUB]
• Rep Deep & Machine Learning: Exemplar-Free Continual Video Action Recognition via Slow-Fast Collaborative Learning. [PUB]
• Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters. [PUB]
• Think Wise, Collaborate Effectively: A Rationale-Aware LLM-Based Recommender with Reinforcement Learning from Collaborative Signals. [PUB]
• Unifying Multi-View Knowledge for Graph Learning via Model Collaboration. [PUB]
• Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent (Abstract Reprint). [PUB]

AI

• Disentangling data distribution for optimal and communication-efficient federated learning. [PUB]
• Federated neural nonparametric point processes. [PUB]

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] [CODE]
• 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 Supervisi