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 3276–3300 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks	Apr 6, 2022	Anomaly DetectionFederated Learning	—Unverified
Federated Learning for Distributed Energy-Efficient Resource Allocation	Apr 20, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified
FedHeN: Federated Learning in Heterogeneous Networks	Jul 7, 2022	Federated Learning	—Unverified
Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information	Nov 12, 2024	Federated Learning	—Unverified
FedHide: Federated Learning by Hiding in the Neighbors	Sep 12, 2024	Federated 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
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
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning	Dec 19, 2022	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
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach	Feb 16, 2024	Federated Learning	—Unverified
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments	Nov 15, 2020	Federated LearningModel Poisoning	—Unverified
Strategic Federated Learning: Application to Smart Meter Data Clustering	Aug 5, 2024	ClusteringFederated Learning	—Unverified
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes	May 12, 2025	Contrastive LearningDiagnostic	—Unverified
Federated Learning for Diffusion Models	Mar 9, 2025	DenoisingFederated Learning	—Unverified
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions	Oct 30, 2024	DiagnosticFederated Learning	—Unverified
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning	Aug 19, 2021	Federated Learning	—Unverified
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives	Oct 10, 2024	Federated Learning	—Unverified
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity	Apr 3, 2023	Federated Learningmodel	—Unverified
Cross-Silo Federated Learning: Challenges and Opportunities	Jun 26, 2022	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