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 1651–1675 of 6771 papers

Title	Date	Tasks	Status
Distributed Contrastive Learning for Medical Image Segmentation	Aug 7, 2022	Contrastive LearningFederated Learning	—Unverified
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing	Jul 11, 2024	Deep Reinforcement LearningEdge-computing	—Unverified
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning	Aug 14, 2023	Federated LearningSSIM	—Unverified
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes	Jul 26, 2021	Federated Learning	—Unverified
Dynamic Fair Federated Learning Based on Reinforcement Learning	Nov 2, 2023	FairnessFederated Learning	—Unverified
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection	Apr 15, 2024	DiagnosticFederated Learning	—Unverified
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats	May 29, 2025	Anomaly DetectionAutonomous Vehicles	—Unverified
Distributed Fixed Point Methods with Compressed Iterates	Dec 20, 2019	Federated Learning	—Unverified
Distributed Learning Approaches for Automated Chest X-Ray Diagnosis	Oct 4, 2021	Federated Learning	—Unverified
Distributed Learning for Time-varying Networks: A Scalable Design	Jul 31, 2021	Federated Learning	—Unverified
Data-driven geophysics: from dictionary learning to deep learning	Jul 13, 2020	Deep LearningDictionary Learning	—Unverified
Distributed Learning for Wi-Fi AP Load Prediction	Apr 22, 2024	Federated LearningKnowledge Distillation	—Unverified
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review	May 22, 2025	Federated LearningGPU	—Unverified
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges	Apr 5, 2021	Decision MakingFederated Learning	—Unverified
Distributed Learning Meets 6G: A Communication and Computing Perspective	Mar 2, 2023	Edge-computingFederated Learning	—Unverified
Distributed Learning on Heterogeneous Resource-Constrained Devices	Jun 9, 2020	Federated LearningReinforcement Learning (RL)	—Unverified
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions	Apr 12, 2022	Federated Learning	—Unverified
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network	Nov 10, 2020	Federated Learning	—Unverified
Distributed Machine Learning and the Semblance of Trust	Dec 21, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics	Jan 3, 2023	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications	Dec 2, 2020	BIG-bench Machine LearningCloud Computing	—Unverified
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees	Oct 7, 2021	Distributed ComputingFederated Learning	—Unverified
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue	Sep 20, 2024	Federated Learning	—Unverified
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation	Jun 7, 2022	Federated LearningVocal Bursts Type Prediction	—Unverified
Data, Competition, and Digital Platforms	Apr 15, 2023	Federated 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