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 1701–1725 of 6771 papers

Title	Date	Tasks	Status	Score
Divergence-aware Federated Self-Supervised Learning	Apr 9, 2022	Federated LearningFederated Unsupervised Learning	CodeCode Available	5
Federated Semi-Supervised Learning with Prototypical Networks	May 27, 2022	Federated Learning	CodeCode Available	5
Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging	Jul 2, 2024	Federated Learning	CodeCode Available	5
End-to-End Verifiable Decentralized Federated Learning	Apr 19, 2024	Federated Learning	CodeCode Available	5
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution	Oct 26, 2022	Data IntegrationFederated Learning	CodeCode Available	5
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data	Jul 29, 2023	ClusteringFederated Learning	CodeCode Available	5
DNN gradient lossless compression: Can GenNorm be the answer?	Nov 15, 2021	Federated Learning	CodeCode Available	5
A Potential Game Perspective in Federated Learning	Nov 18, 2024	Federated Learning	CodeCode Available	5
CrowdGuard: Federated Backdoor Detection in Federated Learning	Oct 14, 2022	Federated LearningOutlier Detection	CodeCode Available	5
Communication-Efficient Federated Learning via Clipped Uniform Quantization	May 22, 2024	Federated LearningQuantization	CodeCode Available	5
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model	Sep 28, 2024	Federated LearningIntrusion Detection	CodeCode Available	5
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning	Jun 6, 2022	Federated LearningGeneralization Bounds	CodeCode Available	5
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budget	Dec 4, 2024	Federated Learning	CodeCode Available	5
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator	Jun 9, 2025	Deep Reinforcement LearningFederated Learning	CodeCode Available	5
Efficient and Robust Regularized Federated Recommendation	Nov 3, 2024	Federated LearningRecommendation Systems	CodeCode Available	5
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability	Sep 26, 2024	Federated Learning	CodeCode Available	5
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection	Dec 31, 2023	DiversityFederated Learning	CodeCode Available	5
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation	Jul 3, 2024	Federated Learning	CodeCode Available	5
Client-specific Property Inference against Secure Aggregation in Federated Learning	Mar 7, 2023	Federated Learning	CodeCode Available	5
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning	Jun 16, 2025	Computational EfficiencyFederated Learning	CodeCode Available	5
Client-Edge-Cloud Hierarchical Federated Learning	May 16, 2019	Federated Learning	CodeCode Available	5
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images	Aug 28, 2022	Federated Learning	CodeCode Available	5
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing	Feb 5, 2025	Federated LearningScheduling	CodeCode Available	5
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning	Oct 2, 2024	FairnessFederated Learning	CodeCode Available	5
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation	Sep 8, 2024	Federated Learning	CodeCode Available	5

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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