Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3251–3300 of 6771 papers

Title	Date	Tasks	Status
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling	Jan 22, 2025	Federated LearningPrivacy Preserving	—Unverified
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix	May 20, 2025	Federated Learning	—Unverified
FedGraph: an Aggregation Method from Graph Perspective	Oct 6, 2022	Federated LearningTumor Segmentation	—Unverified
Communication-Efficient and Personalized Federated Lottery Ticket Learning	Apr 26, 2021	Federated LearningMulti-Task Learning	—Unverified
FedGraph: Federated Graph Learning with Intelligent Sampling	Nov 2, 2021	Deep Reinforcement LearningFederated Learning	—Unverified
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net	Oct 6, 2022	Federated Learning	—Unverified
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations	May 15, 2025	Federated LearningGraph Learning	—Unverified
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings	Oct 25, 2022	Federated LearningRecommendation Systems	—Unverified
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation	Apr 23, 2024	Federated LearningModel Compression	—Unverified
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing	Nov 11, 2021	Edge-computingFederated Learning	—Unverified
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments	Jun 19, 2024	DiversityFairness	—Unverified
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise	Jul 13, 2025	Federated Learningimage-classification	—Unverified
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning	Jul 25, 2023	Activity RecognitionFederated Learning	—Unverified
FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation	Apr 17, 2023	Federated Learning	—Unverified
FedGT: Federated Node Classification with Scalable Graph Transformer	Jan 26, 2024	ClassificationFederated Learning	—Unverified
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning	Oct 16, 2024	Federated LearningTransfer Learning	—Unverified
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain	Apr 16, 2023	Computational EfficiencyFederated Learning	—Unverified
FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs	May 15, 2022	Federated Learning	—Unverified
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval	Jul 12, 2022	Deep HashingFederated Learning	—Unverified
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	Jun 14, 2022	DiversityFederated Learning	—Unverified
FedHB: Hierarchical Bayesian Federated Learning	May 8, 2023	AvgFederated Learning	—Unverified
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks	Nov 5, 2024	backdoor defenseFederated Learning	—Unverified
Communication-Efficient Agnostic Federated Averaging	Apr 6, 2021	Federated LearningLanguage Modeling	—Unverified
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning	Aug 7, 2023	Federated LearningReinforcement Learning (RL)	—Unverified
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models	Oct 7, 2024	Federated Learning	—Unverified
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities	Dec 6, 2024	Federated LearningFew-Shot Learning	—Unverified
DistDD: Distributed Data Distillation Aggregation through Gradient Matching	Oct 11, 2024	Federated LearningNeural Architecture Search	—Unverified
FedHeN: Federated Learning in Heterogeneous Networks	Jul 7, 2022	Federated Learning	—Unverified
A Convergence Theory for Federated Average: Beyond Smoothness	Nov 3, 2022	Edge-computingFederated Learning	—Unverified
FedHide: Federated Learning by Hiding in the Neighbors	Sep 12, 2024	Federated Learning	—Unverified
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data	Aug 14, 2020	Data AugmentationFederated Learning	—Unverified
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity	Jun 21, 2022	Federated Learning	—Unverified
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation	May 24, 2025	Federated Learning	—Unverified
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization	Nov 29, 2021	Distributed ComputingFederated Learning	—Unverified
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring	Dec 14, 2020	Activity RecognitionFederated Learning	—Unverified
Distilling A Universal Expert from Clustered Federated Learning	Jun 25, 2025	Federated Learning	—Unverified
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation	May 27, 2024	Federated LearningPrivacy Preserving	—Unverified
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization	Jun 8, 2022	BenchmarkingFederated Learning	—Unverified
FedHQ: Hybrid Runtime Quantization for Federated Learning	May 17, 2025	Federated LearningQuantization	—Unverified
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent	Oct 4, 2023	Federated LearningHyperparameter Optimization	—Unverified
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities	Dec 2, 2023	Density EstimationFederated Learning	—Unverified
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks	Dec 23, 2024	Federated Learning	—Unverified
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles	Jan 6, 2024	Federated Learning	—Unverified
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes	May 12, 2025	Contrastive LearningDiagnostic	—Unverified
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication	Oct 10, 2023	Federated Learning	—Unverified
Communication Efficient Adaptive Model-Driven Quantum Federated Learning	Jun 5, 2025	Federated Learning	—Unverified
FedImpro: Measuring and Improving Client Update in Federated Learning	Feb 10, 2024	Federated Learning	—Unverified
Distributed collaborative anomalous sound detection by embedding sharing	Mar 25, 2024	Federated Learning	—Unverified
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity	Apr 3, 2023	Federated Learningmodel	—Unverified
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout	Jul 14, 2023	Bayesian InferenceFederated Learning	—Unverified

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified