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 1301–1325 of 6771 papers

Title	Date	Tasks	Status	Score
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study	May 31, 2025	Federated Learning	CodeCode Available	5
Federated Learning From Big Data Over Networks	Oct 27, 2020	Federated Learningregression	CodeCode Available	5
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms	May 26, 2023	Federated LearningSemantic Parsing	CodeCode Available	5
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent Systems	Mar 4, 2025	Autonomous DrivingFederated Learning	CodeCode Available	5
Federated Learning for Mobile Keyboard Prediction	Nov 8, 2018	Federated LearningLanguage Modeling	CodeCode Available	5
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation	Sep 5, 2024	Contrastive LearningFederated Learning	CodeCode Available	5
Federated Learning for Keyword Spotting	Oct 9, 2018	Federated LearningKeyword Spotting	CodeCode Available	5
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models	May 16, 2024	Federated Learning	CodeCode Available	5
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations	May 25, 2024	Decision MakingEpidemiology	CodeCode Available	5
Federated Learning for Data Streams	Jan 4, 2023	Federated Learning	CodeCode Available	5
Federated Learning Client Pruning for Noisy Labels	Nov 11, 2024	Federated Learning	CodeCode Available	5
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling	Sep 7, 2021	Federated Learning	CodeCode Available	5
Federated Stain Normalization for Computational Pathology	Sep 29, 2022	DiversityFederated Learning	CodeCode Available	5
Federated Submodel Optimization for Hot and Cold Data Features	Sep 16, 2021	Federated Learning	CodeCode Available	5
Federated learning compression designed for lightweight communications	Oct 23, 2023	Cloud ComputingFederated Learning	CodeCode Available	5
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment	Jul 22, 2023	Federated Learning	CodeCode Available	5
Federated Learning based on Pruning and Recovery	Mar 16, 2024	Federated Learning	CodeCode Available	5
A Survey on Contribution Evaluation in Vertical Federated Learning	May 3, 2024	Federated LearningSurvey	CodeCode Available	5
Federated Intelligence for Active Queue Management in Inter-Domain Congestion	Jan 8, 2021	Federated LearningManagement	CodeCode Available	5
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models	Dec 18, 2024	Federated Learning	CodeCode Available	5
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data	Apr 26, 2024	ECG ClassificationFederated Learning	CodeCode Available	5
Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation	Aug 26, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5
Federated Impression for Learning with Distributed Heterogeneous Data	Sep 11, 2024	Federated Learning	CodeCode Available	5
Federated Hypergradient Descent	Nov 3, 2022	Federated LearningHyperparameter Optimization	CodeCode Available	5
Federated Frank-Wolfe Algorithm	Aug 19, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5

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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