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 5676–5700 of 6771 papers

Title	Date	Tasks	Status
Blockchain-based Framework for Scalable and Incentivized Federated Learning	Feb 20, 2025	FairnessFederated Learning	—Unverified
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning	Sep 30, 2022	Federated Learning	—Unverified
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning	Jul 25, 2023	Federated Learning	—Unverified
Blockchain-based Secure Client Selection in Federated Learning	May 11, 2022	Federated Learning	—Unverified
Blockchain-based Trustworthy Federated Learning Architecture	Aug 16, 2021	FairnessFederated Learning	—Unverified
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey	May 8, 2023	Federated LearningManagement	—Unverified
Blockchained Federated Learning for Threat Defense	Feb 25, 2021	Federated Learning	—Unverified
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Dec 30, 2024	Edge-computingFairness	—Unverified
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness	Jul 29, 2023	Decision MakingFederated Learning	—Unverified
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks	Feb 7, 2024	Federated LearningGraph Clustering	—Unverified
Blockchain-Enabled Federated Learning Approach for Vehicular Networks	Nov 10, 2023	Federated Learning	—Unverified
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification	Jun 19, 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 Trustworthy Federated Unlearning	Jan 29, 2024	Federated Learning	—Unverified
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain	May 29, 2024	Federated LearningLoad Forecasting	—Unverified
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture	Oct 14, 2020	Federated Learning	—Unverified
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning	Jul 8, 2020	Federated LearningPrivacy Preserving	—Unverified
Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning	Dec 19, 2023	AttributeFederated Learning	—Unverified
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Jul 12, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources	Sep 5, 2022	Federated Learning	—Unverified
Boosting Fairness and Robustness in Over-the-Air Federated Learning	Mar 7, 2024	FairnessFederated Learning	—Unverified
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures	Sep 20, 2024	Domain GeneralizationFederated Learning	—Unverified
Boosting Federated Learning Convergence with Prototype Regularization	Jul 20, 2023	Federated Learning	—Unverified
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations	Apr 11, 2025	Federated LearningMedical Image Analysis	—Unverified

Show:10 25 50

← PrevPage 228 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