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 2001–2025 of 6771 papers

Title	Date	Tasks	Status
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning	Feb 18, 2021	Federated LearningMulti-Task Learning	—Unverified
Entity Resolution and Federated Learning get a Federated Resolution	Mar 11, 2018	Entity ResolutionFederated Learning	—Unverified
Byzantine-Robust Decentralized Federated Learning	Jun 14, 2024	Federated Learning	—Unverified
EPIC: Enhancing Privacy through Iterative Collaboration	Nov 7, 2024	Federated LearningPrivacy Preserving	—Unverified
Epidemic Decision-making System Based Federated Reinforcement Learning	Nov 3, 2023	Decision MakingFederated Learning	—Unverified
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization	Jul 22, 2024	Federated Learning	—Unverified
Efficient Privacy Preserving Edge Computing Framework for Image Classification	May 10, 2020	ClassificationData Compression	—Unverified
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks	Mar 26, 2023	Edge-computingFederated Learning	—Unverified
Equitable Federated Learning with Activation Clustering	Oct 24, 2024	ClusteringFederated Learning	—Unverified
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment	Sep 2, 2023	Federated Learning	—Unverified
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination	Mar 30, 2022	Face GenerationFace Hallucination	—Unverified
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience	Mar 17, 2021	Federated Learning	—Unverified
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression	Oct 29, 2023	Federated Learning	—Unverified
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning	Feb 12, 2022	Distributed OptimizationFederated Learning	—Unverified
Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping	Jan 1, 2024	ClusteringFederated Learning	—Unverified
ESMFL: Efficient and Secure Models for Federated Learning	Sep 3, 2020	Federated LearningPrivacy Preserving	—Unverified
A distillation-based approach integrating continual learning and federated learning for pervasive services	Sep 9, 2021	Activity RecognitionContinual Learning	—Unverified
A Coalition Formation Game Approach for Personalized Federated Learning	Feb 5, 2022	DiversityFederated Learning	—Unverified
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation	Aug 29, 2023	Federated Learning	—Unverified
Efficient Model Compression for Hierarchical Federated Learning	May 27, 2024	Edge-computingFederated Learning	—Unverified
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning	Jun 8, 2023	DiversityFederated Learning	—Unverified
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization	Mar 22, 2024	Domain GeneralizationFederated Learning	—Unverified
Byzantine-Robust and Privacy-Preserving Framework for FedML	May 5, 2021	Federated LearningPrivacy Preserving	—Unverified
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning	Dec 16, 2024	Federated LearningPrivacy Preserving	—Unverified
Efficient Language Model Architectures for Differentially Private Federated Learning	Mar 12, 2024	Computational EfficiencyFederated 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