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 3451–3475 of 6771 papers

Title	Date	Tasks	Status	Hype
Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain	May 16, 2023	Federated LearningPrivacy Preserving	—Unverified	0
A novel parameter decoupling approach of personalized federated learning for image analysis	May 15, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
Adaptive Federated Pruning in Hierarchical Wireless Networks	May 15, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning	May 15, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning	May 15, 2023	Federated LearningVertical Federated Learning	—Unverified	0
Federated Learning over Harmonized Data Silos	May 15, 2023	Data IntegrationFederated Learning	—Unverified	0
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments	May 15, 2023	Federated LearningScheduling	—Unverified	0
A Survey of Federated Evaluation in Federated Learning	May 14, 2023	Federated LearningSurvey	—Unverified	0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning	May 14, 2023	Federated LearningPrediction	—Unverified	0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling	May 14, 2023	Distributed OptimizationFederated Learning	—Unverified	0
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data	May 13, 2023	Federated Learning	CodeCode Available	0
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction	May 13, 2023	Federated LearningPrivacy Preserving	—Unverified	0
GPFedRec: Graph-guided Personalization for Federated Recommendation	May 13, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus	May 13, 2023	Distributed OptimizationFederated Learning	—Unverified	0
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning	May 11, 2023	Federated Learning	—Unverified	0
Multi-Tier Client Selection for Mobile Federated Learning Networks	May 11, 2023	Federated Learning	—Unverified	0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training	May 11, 2023	Federated LearningGraph Learning	—Unverified	0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems	May 11, 2023	Federated LearningNeural Architecture Search	—Unverified	0
Self-Supervised Federated Learning for Fast MR Imaging	May 10, 2023	Federated LearningImage Reconstruction	—Unverified	0
Securing Distributed SGD against Gradient Leakage Threats	May 10, 2023	Federated Learning	CodeCode Available	0
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference	May 10, 2023	Federated Learning	—Unverified	0
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature	May 10, 2023	Federated Learning	—Unverified	0
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment	May 10, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	0
XTab: Cross-table Pretraining for Tabular Transformers	May 10, 2023	AutoMLFederated Learning	CodeCode Available	1
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation	May 9, 2023	Federated LearningPrivacy Preserving	—Unverified	0

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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