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 3251–3275 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
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models	Oct 7, 2024	Federated Learning	—Unverified
FedGraph: Federated Graph Learning with Intelligent Sampling	Nov 2, 2021	Deep Reinforcement LearningFederated Learning	—Unverified
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities	Dec 6, 2024	Federated LearningFew-Shot 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 Convergence Theory for Federated Average: Beyond Smoothness	Nov 3, 2022	Edge-computingFederated Learning	—Unverified
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise	Jul 13, 2025	Federated Learningimage-classification	—Unverified
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization	Oct 4, 2024	Federated Learning	—Unverified
Communication Efficient Adaptive Model-Driven Quantum Federated Learning	Jun 5, 2025	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
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout	Jul 14, 2023	Bayesian InferenceFederated 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
A Real-time Contribution Measurement Method for Participants in Federated Learning	Sep 28, 2020	Federated Learning	—Unverified
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles	Aug 5, 2021	energy managementFederated Learning	—Unverified
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble	Mar 19, 2025	Bayesian InferenceFederated Learning	—Unverified
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks	Nov 28, 2023	Federated 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