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 2301–2325 of 6771 papers

Title	Date	Tasks	Status
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data	Dec 1, 2021	Federated Learning	—Unverified
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service	Mar 15, 2023	Federated LearningManagement	—Unverified
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network	May 15, 2025	Autonomous VehiclesFederated Learning	—Unverified
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency	Jan 22, 2020	Federated LearningPrivacy Preserving	—Unverified
Comparative assessment of federated and centralized machine learning	Feb 3, 2022	BIG-bench Machine LearningFederated Learning	—Unverified
Accelerating Federated Learning in Heterogeneous Data and Computational Environments	Aug 25, 2020	Federated Learning	—Unverified
1-D CNN-Based Online Signature Verification with Federated Learning	Jun 6, 2024	Federated Learning	—Unverified
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems	Jun 15, 2023	Community DetectionFederated Learning	—Unverified
CommunityAI: Towards Community-based Federated Learning	Nov 29, 2023	Federated Learning	—Unverified
Communication Trade-offs in Federated Learning of Spiking Neural Networks	Feb 27, 2023	Federated Learning	—Unverified
Federated Learning of Neural ODE Models with Different Iteration Counts	Aug 19, 2022	Federated Learning	—Unverified
A Survey of Federated Learning for Connected and Automated Vehicles	Mar 19, 2023	Federated LearningMotion Planning	—Unverified
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection	Nov 4, 2021	Federated LearningIntrusion Detection	—Unverified
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach	May 2, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation	Aug 15, 2024	Federated LearningScheduling	—Unverified
A Survey of Federated Evaluation in Federated Learning	May 14, 2023	Federated LearningSurvey	—Unverified
A Federated Learning Framework for Non-Intrusive Load Monitoring	Apr 4, 2021	Federated LearningModel Selection	—Unverified
Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates	Aug 12, 2021	DecoderFederated Learning	—Unverified
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks	Apr 16, 2025	Computational EfficiencyFederated Learning	—Unverified
Towards Fairness-Aware Federated Learning	Nov 2, 2021	FairnessFederated Learning	—Unverified
A Survey for Large Language Models in Biomedicine	Aug 29, 2024	DiagnosticDrug Discovery	—Unverified
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models	May 1, 2025	Federated Learning	—Unverified
ActPerFL: Active Personalized Federated Learning	May 1, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples	Mar 28, 2023	Federated LearningVertical Federated Learning	—Unverified
A Federated Learning Framework for Healthcare IoT devices	May 7, 2020	Federated Learning	—Unverified

Show:10 25 50

← PrevPage 93 of 271Next →

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