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 3026–3050 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Ranking Browser History Suggestions	Nov 26, 2019	Federated LearningPrivacy Preserving	—Unverified
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information	May 9, 2025	Federated Learning	—Unverified
Age-of-Gradient Updates for Federated Learning over Random Access Channels	Oct 15, 2024	Federated Learning	—Unverified
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks	Oct 23, 2021	ClassificationFederated Learning	—Unverified
Federated Multi-Target Domain Adaptation	Aug 17, 2021	Domain AdaptationFederated Learning	—Unverified
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning	Apr 9, 2022	Federated Learning	—Unverified
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health	Aug 24, 2021	Federated LearningPrivacy Preserving	—Unverified
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning	Mar 14, 2024	Federated LearningPersonalized Federated Learning	—Unverified
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey	Mar 18, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications	Apr 21, 2023	Federated Learning	—Unverified
Federated Multi-view Matrix Factorization for Personalized Recommendations	Apr 8, 2020	Federated Learning	—Unverified
Federated Multi-View Synthesizing for Metaverse	Dec 18, 2023	Domain AdaptationFederated Learning	—Unverified
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems	Jan 8, 2022	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion	Mar 5, 2025	Federated Learning	—Unverified
Federated Learning for Physical Layer Design	Feb 23, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Open Banking	Aug 24, 2021	Federated Learning	—Unverified
Federated Neural Architecture Search with Model-Agnostic Meta Learning	Apr 8, 2025	Federated LearningMeta-Learning	—Unverified
Federated Neural Collaborative Filtering	Jun 2, 2021	Collaborative FilteringFederated Learning	—Unverified
Federated Neural Compression Under Heterogeneous Data	May 25, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update	Jul 18, 2022	Federated Learning	—Unverified
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain	Jul 8, 2024	Federated LearningIndoor Localization	—Unverified
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning	May 9, 2024	BenchmarkingFederated Learning	—Unverified
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment	Jan 1, 2021	Federated LearningPrivacy Preserving	—Unverified
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing	Nov 13, 2023	Federated Learning	—Unverified
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction	Jun 18, 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