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 2001–2025 of 6771 papers

Title	Date	Tasks	Status
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap	Jun 25, 2024	Entity AlignmentEntity Resolution	—Unverified
Entity Resolution and Federated Learning get a Federated Resolution	Mar 11, 2018	Entity ResolutionFederated Learning	—Unverified
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN	Sep 29, 2021	Federated Learningspeech-recognition	—Unverified
EPIC: Enhancing Privacy through Iterative Collaboration	Nov 7, 2024	Federated LearningPrivacy Preserving	—Unverified
Epidemic Decision-making System Based Federated Reinforcement Learning	Nov 3, 2023	Decision MakingFederated Learning	—Unverified
Centroid Approximation for Byzantine-Tolerant Federated Learning	Jun 18, 2025	Distributed ComputingFederated Learning	—Unverified
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning	Jun 1, 2023	Federated LearningPrivacy Preserving	—Unverified
A Systematic Review of Federated Generative Models	May 26, 2024	Federated Learning	—Unverified
Equitable Federated Learning with Activation Clustering	Oct 24, 2024	ClusteringFederated Learning	—Unverified
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment	Sep 2, 2023	Federated Learning	—Unverified
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination	Mar 30, 2022	Face GenerationFace Hallucination	—Unverified
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience	Mar 17, 2021	Federated Learning	—Unverified
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression	Oct 29, 2023	Federated Learning	—Unverified
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning	Feb 12, 2022	Distributed OptimizationFederated Learning	—Unverified
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates	Jun 21, 2022	Federated Learning	—Unverified
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication	May 12, 2024	Face DetectionFederated Learning	—Unverified
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning	May 30, 2024	Federated LearningRepresentation Learning	—Unverified
Estimation of Individual Device Contributions for Incentivizing Federated Learning	Sep 20, 2020	Federated Learning	—Unverified
CFLIT: Coexisting Federated Learning and Information Transfer	Jul 26, 2022	Federated Learning	—Unverified
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI	Aug 4, 2020	ClusteringFederated Learning	—Unverified
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning	Jun 8, 2023	DiversityFederated Learning	—Unverified
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models	May 22, 2024	DecoderFederated Learning	—Unverified
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective	Dec 1, 2020	ArticlesFederated Learning	—Unverified
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective	Jul 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees	Apr 30, 2025	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