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 4676–4700 of 6771 papers

Title	Date	Tasks	Status
Gradient Inversion of Federated Diffusion Models	May 30, 2024	Federated LearningPrivacy Preserving	—Unverified
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates	Apr 15, 2023	Federated Learning	—Unverified
Gradient Masked Averaging for Federated Learning	Jan 28, 2022	Federated LearningOut-of-Distribution Generalization	—Unverified
Gradient Masked Federated Optimization	Apr 21, 2021	Federated Learning	—Unverified
Gradient Obfuscation Gives a False Sense of Security in Federated Learning	Jun 8, 2022	Federated Learningimage-classification	—Unverified
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning	Jan 8, 2025	Federated Learning	—Unverified
Gradient Sparification for Asynchronous Distributed Training	Oct 24, 2019	Federated LearningStochastic Optimization	—Unverified
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy	Apr 9, 2023	Federated LearningScheduling	—Unverified
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning	Mar 4, 2020	DenoisingFederated Learning	—Unverified
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model	Jun 23, 2025	Federated LearningLanguage Modeling	—Unverified
Gradual Federated Learning with Simulated Annealing	Oct 11, 2021	Federated Learning	—Unverified
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model	Aug 29, 2020	Dimensionality ReductionFederated Learning	—Unverified
Graph-Assisted Communication-Efficient Ensemble Federated Learning	Feb 27, 2022	Federated Learning	—Unverified
Graph Federated Learning Based on the Decentralized Framework	Jul 19, 2023	Federated LearningGraph Learning	—Unverified
Graph Federated Learning Based Proactive Content Caching in Edge Computing	Feb 7, 2025	Edge-computingFederated Learning	—Unverified
Graph Federated Learning with Hidden Representation Sharing	Dec 23, 2022	Federated Learning	—Unverified
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs	Dec 8, 2020	Federated LearningGeneral Classification	—Unverified
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery	Apr 11, 2023	Drug DiscoveryFederated Learning	—Unverified
Green Federated Learning	Mar 26, 2023	Federated Learning	—Unverified
Green Federated Learning: A new era of Green Aware AI	Sep 19, 2024	Federated LearningNavigate	—Unverified
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks	Apr 30, 2023	Federated Learning	—Unverified
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design	Jul 19, 2022	Federated LearningQuantization	—Unverified
Ground-Assisted Federated Learning in LEO Satellite Constellations	Sep 3, 2021	Federated Learning	—Unverified
Grounding Foundation Models through Federated Transfer Learning: A General Framework	Nov 29, 2023	Federated LearningPrivacy Preserving	—Unverified
Group Personalized Federated Learning	Oct 4, 2022	Federated LearningPersonalized Federated 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