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 4626–4650 of 6771 papers

Title	Date	Tasks	Status
Fusion of Federated Learning and Industrial Internet of Things: A Survey	Jan 4, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review	Sep 24, 2024	Federated LearningPrivacy Preserving	—Unverified
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering	Jun 8, 2023	Anomaly DetectionClustering	—Unverified
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning	Oct 11, 2024	Federated LearningKnowledge Distillation	—Unverified
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction	Feb 10, 2022	Federated LearningVertical Federated Learning	—Unverified
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?	Dec 10, 2024	Federated Learning	—Unverified
GAN-based Vertical Federated Learning for Label Protection in Binary Classification	Feb 4, 2023	Binary ClassificationFederated Learning	—Unverified
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning	May 31, 2024	Contrastive LearningFederated Learning	—Unverified
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation	Mar 17, 2025	Federated LearningImage Classification	—Unverified
GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS	Jun 4, 2025	Federated LearningPrivacy Preserving	—Unverified
GeFL: Model-Agnostic Federated Learning with Generative Models	Dec 24, 2024	Federated Learningmodel	—Unverified
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning	Apr 20, 2025	Federated Learning	—Unverified
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles	Jul 10, 2023	Federated Learningparameter estimation	—Unverified
Generalization in Federated Learning: A Conditional Mutual Information Framework	Mar 6, 2025	Federated LearningPrivacy Preserving	—Unverified
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations	May 26, 2025	Federated LearningPersonalized Federated Learning	—Unverified
Generalized Policy Learning for Smart Grids: FL TRPO Approach	Mar 27, 2024	energy managementFederated Learning	—Unverified
Generation of Synthetic Electronic Health Records Using a Federated GAN	Sep 6, 2021	Federated LearningGenerative Adversarial Network	—Unverified
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future	Jul 25, 2024	Federated Learning	—Unverified
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning	Dec 2, 2022	Data AugmentationEdge-computing	—Unverified
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning	Oct 21, 2024	ClusteringFederated Learning	—Unverified
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning	Apr 20, 2023	Federated Learningimage-classification	—Unverified
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV	Apr 23, 2022	Anomaly DetectionContinual Learning	—Unverified
γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning	May 18, 2025	Federated Learning	—Unverified
GFL: A Decentralized Federated Learning Framework Based On Blockchain	Oct 21, 2020	Data PoisoningFederated Learning	—Unverified
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning	Aug 5, 2021	FairnessFederated Learning	—Unverified

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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