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 976–1000 of 6771 papers

Title	Date	Tasks	Status
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction	Nov 22, 2020	Federated Learning	—Unverified
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model	Feb 16, 2022	Federated LearningLanguage Modeling	—Unverified
FLBench: A Benchmark Suite for Federated Learning	Aug 17, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model	Mar 10, 2025	Federated LearningLanguage Modeling	—Unverified
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning	Jun 12, 2023	Federated LearningPrivacy Preserving	—Unverified
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities	Jan 20, 2022	Federated LearningPrivacy Preserving	—Unverified
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation	Apr 14, 2024	Earth ObservationFederated Learning	—Unverified
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning	Apr 29, 2024	Bayesian OptimizationBinary Classification	—Unverified
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning	Nov 14, 2020	ClassificationFederated Learning	—Unverified
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications	Feb 1, 2023	Data PoisoningDecoder	—Unverified
CC-FedAvg: Computationally Customized Federated Averaging	Dec 28, 2022	Federated Learning	—Unverified
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging	Jul 16, 2024	DiversityFederated Learning	—Unverified
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning	Apr 8, 2022	Federated LearningMedical Image Analysis	—Unverified
An Empirical Study of Vulnerability Detection using Federated Learning	Nov 25, 2024	Federated LearningVulnerability Detection	—Unverified
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering	Jul 17, 2024	Activity RecognitionClustering	—Unverified
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points	Jun 29, 2021	Federated Learningglobal-optimization	—Unverified
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model	Oct 20, 2023	Federated Learning	—Unverified
EcoLearn: Optimizing the Carbon Footprint of Federated Learning	Oct 27, 2023	Federated Learning	—Unverified
CELEST: Federated Learning for Globally Coordinated Threat Detection	May 23, 2022	Active LearningFederated Learning	—Unverified
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning	Jul 30, 2024	Federated Learning	—Unverified
Anonymizing Data for Privacy-Preserving Federated Learning	Feb 21, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Celtibero: Robust Layered Aggregation for Federated Learning	Aug 26, 2024	Federated Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting	Oct 12, 2024	Federated Learning	—Unverified
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy	Mar 26, 2025	Federated Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training	Apr 20, 2024	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