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 1076–1100 of 6771 papers

Title	Date	Tasks	Status
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning	Jan 27, 2021	Federated LearningOpen-Ended Question Answering	—Unverified
Blockchain-enabled Trustworthy Federated Unlearning	Jan 29, 2024	Federated Learning	—Unverified
An Efficient and Multi-private Key Secure Aggregation for Federated Learning	Jun 15, 2023	Federated Learning	—Unverified
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare	May 31, 2025	Federated LearningPrivacy Preserving	—Unverified
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification	Jun 19, 2023	Federated Learning	—Unverified
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee	Nov 3, 2020	Distributed ComputingFairness	—Unverified
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration	Oct 2, 2024	Federated LearningPersonalized Federated Learning	—Unverified
A blockchain-orchestrated Federated Learning architecture for healthcare consortia	Oct 12, 2019	Federated LearningPrivacy Preserving	—Unverified
Blockchain-Enabled Federated Learning Approach for Vehicular Networks	Nov 10, 2023	Federated Learning	—Unverified
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks	Feb 7, 2024	Federated LearningGraph Clustering	—Unverified
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models	Jul 20, 2024	DiversityEpidemiology	—Unverified
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness	Jul 29, 2023	Decision MakingFederated Learning	—Unverified
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning	Apr 15, 2024	Federated Learning	—Unverified
Achieving Linear Speedup in Non-IID Federated Bilevel Learning	Feb 10, 2023	Bilevel OptimizationFederated Learning	—Unverified
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data	Dec 1, 2021	Federated Learning	—Unverified
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Dec 30, 2024	Edge-computingFairness	—Unverified
Blockchained Federated Learning for Threat Defense	Feb 25, 2021	Federated Learning	—Unverified
An Auction-based Marketplace for Model Trading in Federated Learning	Feb 2, 2024	Federated LearningMarketing	—Unverified
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey	May 8, 2023	Federated LearningManagement	—Unverified
Blockchain-based Trustworthy Federated Learning Architecture	Aug 16, 2021	FairnessFederated Learning	—Unverified
Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs	Oct 26, 2024	Domain GeneralizationFederated Learning	—Unverified
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization	Mar 24, 2022	Continual LearningFederated Learning	—Unverified
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay	Jul 17, 2024	Federated Learning	—Unverified
Comparing privacy notions for protection against reconstruction attacks in machine learning	Feb 6, 2025	Federated Learning	—Unverified
Complex-valued Federated Learning with Differential Privacy and MRI Applications	Oct 7, 2021	Federated LearningMRI Reconstruction	—Unverified

Show:10 25 50

← PrevPage 44 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