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 2851–2875 of 6771 papers

Title	Date	Tasks	Status
Federated Learning: From Theory to Practice	May 25, 2025	Distributed OptimizationFederated Learning	—Unverified
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples	Jan 6, 2021	Federated Learning	—Unverified
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis	Jan 29, 2021	Cloud ComputingDimensionality Reduction	—Unverified
Federated Learning over Wireless Fading Channels	Jul 23, 2019	Federated Learning	—Unverified
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources	Oct 22, 2021	Federated LearningScheduling	—Unverified
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory	May 22, 2024	class-incremental learningClass Incremental Learning	—Unverified
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach	Feb 16, 2021	Federated Learning	—Unverified
Federated Learning Priorities Under the European Union Artificial Intelligence Act	Feb 5, 2024	Federated LearningPosition	—Unverified
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape	May 6, 2024	Federated LearningPrivacy Preserving	—Unverified
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap	Aug 21, 2023	Federated Learning	—Unverified
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach	Sep 21, 2022	Contrastive LearningFederated Learning	—Unverified
Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC	Jan 28, 2025	Federated LearningISAC	—Unverified
Data-Free Evaluation of User Contributions in Federated Learning	Aug 24, 2021	Federated LearningProduct Recommendation	—Unverified
A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks	Jan 25, 2025	Federated Learning	—Unverified
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices	May 26, 2023	Federated Learning	—Unverified
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things	Jun 22, 2024	Decision MakingDemand Forecasting	—Unverified
Federated Learning under Attack: Improving Gradient Inversion for Batch of Images	Sep 26, 2024	Federated Learning	—Unverified
Federated Learning under Covariate Shifts with Generalization Guarantees	Jun 8, 2023	Federated Learning	—Unverified
Federated Learning from Molecules to Processes: A Perspective	Jun 23, 2025	Federated Learning	—Unverified
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation	Mar 30, 2023	Federated Learning	—Unverified
Federated Learning under Importance Sampling	Dec 14, 2020	Federated Learning	—Unverified
Federated Learning Framework via Distributed Mutual Learning	Mar 3, 2025	Federated Learning	—Unverified
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis	Oct 22, 2023	Federated LearningPrivacy Preserving	—Unverified
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions	Dec 11, 2024	Federated LearningRecommendation Systems	—Unverified
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study	Oct 15, 2024	Anomaly DetectionFederated 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