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 1376–1400 of 6771 papers

Title	Date	Tasks	Status	Score
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data	Jun 9, 2025	Federated Learning	CodeCode Available	5
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data	Nov 12, 2024	FairnessFederated Learning	CodeCode Available	5
A Federated Learning Benchmark for Drug-Target Interaction	Feb 15, 2023	Federated LearningPrivacy Preserving	CodeCode Available	5
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning	Jul 21, 2021	Federated Learning	CodeCode Available	5
FedQV: Leveraging Quadratic Voting in Federated Learning	Jan 2, 2024	Decision MakingFederated Learning	CodeCode Available	5
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data	Oct 28, 2024	Federated Learning	CodeCode Available	5
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market	Feb 23, 2023	DiversityEnsemble Learning	CodeCode Available	5
FedRec: Federated Learning of Universal Receivers over Fading Channels	Nov 14, 2020	Federated Learning	CodeCode Available	5
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model	Jun 29, 2025	Brain Tumor SegmentationFederated Learning	CodeCode Available	5
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment	May 24, 2022	Federated Learning	CodeCode Available	5
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT	Feb 3, 2022	Federated Learning	CodeCode Available	5
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement	Jan 20, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5
Data Leakage in Federated Averaging	Jun 24, 2022	Federated Learning	CodeCode Available	5
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data	Jul 14, 2021	Federated Learning	CodeCode Available	5
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment	May 10, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning	Aug 14, 2023	Brain Computer InterfaceEEG	CodeCode Available	5
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates	Jun 8, 2023	Federated Learning	CodeCode Available	5
FedScore: A privacy-preserving framework for federated scoring system development	Mar 1, 2023	Federated LearningModel Selection	CodeCode Available	5
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning	Dec 5, 2024	Federated Learningimage-classification	CodeCode Available	5
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning	Dec 5, 2024	Federated LearningKnowledge Distillation	CodeCode Available	5
Federated Active Learning for Target Domain Generalisation	Dec 4, 2023	Active LearningFederated Learning	CodeCode Available	5
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration	May 25, 2025	Contrastive LearningEdge-computing	CodeCode Available	5
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update	Aug 20, 2021	Federated Learning	CodeCode Available	5
Federated clustering with GAN-based data synthesis	Oct 29, 2022	ClusteringFederated Learning	CodeCode Available	5
Federated Frank-Wolfe Algorithm	Aug 19, 2024	Federated LearningPrivacy Preserving	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