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 901–925 of 6771 papers

Title	Date	Tasks	Status	Hype
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis	Dec 27, 2024	DiagnosticFederated Learning	CodeCode Available	0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks	Dec 26, 2024	Data AugmentationFederated Learning	—Unverified	0
Effective and secure federated online learning to rank	Dec 26, 2024	Federated LearningLearning-To-Rank	—Unverified	0
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning	Dec 25, 2024	counterfactualDistributed Optimization	CodeCode Available	1
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach	Dec 25, 2024	Federated LearningImage Segmentation	—Unverified	0
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints	Dec 25, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis	Dec 24, 2024	Contrastive LearningFederated Learning	—Unverified	0
FedGIG: Graph Inversion from Gradient in Federated Learning	Dec 24, 2024	Federated LearningGraph Learning	—Unverified	0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack	Dec 24, 2024	Adversarial AttackFederated Learning	—Unverified	0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor	Dec 24, 2024	Continual LearningFederated Learning	—Unverified	0
GeFL: Model-Agnostic Federated Learning with Generative Models	Dec 24, 2024	Federated Learningmodel	—Unverified	0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings	Dec 23, 2024	Computational EfficiencyFederated Learning	—Unverified	0
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks	Dec 23, 2024	Federated Learning	—Unverified	0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction	Dec 23, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning	Dec 23, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning	Dec 23, 2024	Federated LearningKnowledge Distillation	—Unverified	0
FedTLU: Federated Learning with Targeted Layer Updates	Dec 23, 2024	Federated LearningLanguage Modeling	—Unverified	0
FedCross: Intertemporal Federated Learning Under Evolutionary Games	Dec 22, 2024	Federated Learning	—Unverified	0
Data value estimation on private gradients	Dec 22, 2024	Data ValuationFederated Learning	—Unverified	0
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation	Dec 21, 2024	Federated Learning	—Unverified	0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation	Dec 21, 2024	Computational EfficiencyFederated Learning	—Unverified	0
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training	Dec 21, 2024	Federated Learningparameter-efficient fine-tuning	CodeCode Available	0
Federal Learning Framework for Quality Evaluation of Blastomere Cleavage	Dec 21, 2024	Federated Learning	—Unverified	0
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions	Dec 21, 2024	Federated LearningPrivacy Preserving	—Unverified	0
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game	Dec 20, 2024	AttributeFederated Learning	—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