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 6526–6550 of 6771 papers

Title	Date	Tasks	Status
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top	Jun 1, 2022	Federated Learning	CodeCode Available
NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data	Jun 28, 2023	Federated LearningHuman Detection	CodeCode Available
Client-Edge-Cloud Hierarchical Federated Learning	May 16, 2019	Federated Learning	CodeCode Available
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs	Jan 18, 2024	ClusteringDiversity	CodeCode Available
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning	Jun 16, 2025	Computational EfficiencyFederated Learning	CodeCode Available
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data	Jun 9, 2021	Classifier calibrationFederated Learning	CodeCode Available
Synergizing Foundation Models and Federated Learning: A Survey	Jun 18, 2024	Federated LearningSurvey	CodeCode Available
Hierarchical Federated Learning with Multi-Timescale Gradient Correction	Sep 27, 2024	Federated Learning	CodeCode Available
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning	Jun 5, 2024	Federated Learning	CodeCode Available
Towards Federated Learning With Byzantine-Robust Client Weighting	Apr 10, 2020	Federated Learning	CodeCode Available
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations	Mar 18, 2021	Federated Learning	CodeCode Available
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints	Jul 17, 2025	Federated LearningImage Generation	CodeCode Available
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity	Jun 23, 2023	Federated Learning	CodeCode Available
Certified Robustness in Federated Learning	Jun 6, 2022	Federated Learning	CodeCode Available
Central Server Free Federated Learning over Single-sided Trust Social Networks	Oct 11, 2019	Federated Learning	CodeCode Available
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models	Mar 11, 2025	Federated LearningPrivacy Preserving	CodeCode Available
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective	Jan 6, 2025	Federated LearningRecommendation Systems	CodeCode Available
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies	Sep 9, 2021	BenchmarkingFederated Learning	CodeCode Available
Privacy Amplification by Decentralization	Dec 9, 2020	Federated Learning	CodeCode Available
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation	Mar 2, 2021	Federated Learning	CodeCode Available
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions	Feb 24, 2019	Bayesian OptimizationBIG-bench Machine Learning	CodeCode Available
Non-parametric regularization for class imbalance federated medical image classification	Jul 17, 2024	Federated Learningimage-classification	CodeCode Available
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective	Oct 6, 2021	Federated Learning	CodeCode Available
Early prediction of the risk of ICU mortality with Deep Federated Learning	Dec 1, 2022	Federated LearningICU Mortality	CodeCode Available
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning	May 31, 2024	Federated LearningNavigate	CodeCode Available

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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