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 3976–4000 of 6771 papers

Title	Date	Tasks	Status
Towards Federated Graph Learning for Collaborative Financial Crimes Detection	Sep 19, 2019	Federated LearningGraph Learning	—Unverified
Towards Federated Learning-Enabled Visible Light Communication in 6G Systems	Oct 7, 2021	Federated Learning	—Unverified
Towards Federated Learning on Time-Evolving Heterogeneous Data	Dec 25, 2021	DiversityFederated Learning	—Unverified
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout	Sep 11, 2023	Federated LearningRepresentation Learning	—Unverified
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point	Jul 2, 2024	Federated Learning	—Unverified
Towards Federated Long-Tailed Learning	Jun 30, 2022	Federated Learning	—Unverified
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning	Dec 7, 2022	Fault DetectionFederated Learning	—Unverified
Towards Flexible Device Participation in Federated Learning	Jun 12, 2020	Federated Learning	—Unverified
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation	Sep 29, 2021	Federated LearningMeta-Learning	—Unverified
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset	May 31, 2025	Federated Learningimage-classification	—Unverified
Towards Group Learning: Distributed Weighting of Experts	Jun 3, 2022	Ensemble LearningFederated Learning	—Unverified
Towards Heterogeneous Clients with Elastic Federated Learning	Jun 17, 2021	Federated Learning	—Unverified
Towards Industrial Private AI: A two-tier framework for data and model security	Jul 27, 2021	Federated Learning	—Unverified
Towards Interpretable Federated Learning	Feb 27, 2023	Federated Learning	—Unverified
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients	Oct 3, 2024	DisentanglementFederated Learning	—Unverified
Toward Smart Security Enhancement of Federated Learning Networks	Aug 19, 2020	Deep Reinforcement LearningFederated Learning	—Unverified
Towards Model Agnostic Federated Learning Using Knowledge Distillation	Oct 28, 2021	Federated LearningKnowledge Distillation	—Unverified
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling	Sep 12, 2022	Data ValuationFederated Learning	—Unverified
Towards More Efficient Federated Learning with Better Optimization Objects	Aug 19, 2021	Federated Learning	—Unverified
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training	May 24, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Holistic analysis on the sustainability of Federated Learning across AI product lifecycle	Dec 22, 2023	Federated LearningManagement	—Unverified
Towards Multi-Objective Statistically Fair Federated Learning	Jan 24, 2022	Data PoisoningFairness	—Unverified
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach	Apr 27, 2021	Anomaly DetectionFederated Learning	—Unverified
Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning	Dec 15, 2020	Federated LearningSelf-Learning	—Unverified
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning	Apr 7, 2025	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