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 6751–6771 of 6771 papers

Title	Date	Tasks	Status
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler	May 24, 2024	Federated Learning	—Unverified
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning	Jan 1, 2025	Federated LearningPersonalized Federated Learning	—Unverified
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data	Apr 16, 2025	Computational EfficiencyFederated Learning	—Unverified
FedCAT: Towards Accurate Federated Learning via Device Concatenation	Feb 23, 2022	Federated Learning	—Unverified
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning	Apr 16, 2022	Collision AvoidanceFederated Learning	—Unverified
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction	Sep 30, 2022	Federated LearningPrivacy Preserving	—Unverified
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning	Jun 4, 2021	Data ValuationFederated Learning	—Unverified
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity	Apr 14, 2024	ClusteringFederated Learning	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning	Jun 9, 2025	Federated LearningScheduling	—Unverified
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features	Aug 30, 2023	Federated LearningRepresentation Learning	—Unverified
FedCLEAN: byzantine defense by CLustering Errors of Activation maps in Non-IID federated learning environments	Jan 21, 2025	Federated Learning	—Unverified
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy	Nov 15, 2024	ClassificationContinual Learning	—Unverified
FedCliP: Federated Learning with Client Pruning	Jan 17, 2023	Federated Learning	—Unverified
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling	Sep 22, 2020	Federated Learning	—Unverified
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering	Mar 7, 2024	Federated Learning	—Unverified
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering	Jul 9, 2024	Federated Learning	—Unverified
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning	Jul 17, 2023	Federated Learning	—Unverified
Self-supervised On-device Federated Learning from Unlabeled Streams	Dec 2, 2022	Contrastive LearningFederated Learning	—Unverified
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks	Nov 15, 2023	Federated LearningModel Compression	—Unverified
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning	Apr 16, 2021	Data PoisoningFederated 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