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 3526–3550 of 6771 papers

Title	Date	Tasks	Status
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios	Jul 19, 2022	Federated LearningQ-Learning	—Unverified
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme	Nov 5, 2022	Federated Learning	—Unverified
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning	May 12, 2024	Deep Reinforcement LearningFederated Learning	—Unverified
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks	Mar 7, 2024	DiversityFederated Learning	—Unverified
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts	Oct 17, 2024	Depression DetectionFederated Learning	—Unverified
On-device Federated Learning with Flower	Apr 7, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning	Jan 22, 2024	Computational EfficiencyFederated Learning	—Unverified
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations	Aug 25, 2022	Federated Learning	—Unverified
One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization	Oct 30, 2022	Federated Learning	—Unverified
One Node Per User: Node-Level Federated Learning for Graph Neural Networks	Sep 29, 2024	Federated LearningGraph Learning	—Unverified
One-Shot Clustering for Federated Learning	Mar 6, 2025	ClusteringFederated Learning	—Unverified
One-shot Empirical Privacy Estimation for Federated Learning	Feb 6, 2023	Federated Learning	—Unverified
One-Shot Federated Learning	Feb 28, 2019	Ensemble LearningFederated Learning	—Unverified
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes	May 21, 2023	Federated LearningKnowledge Distillation	—Unverified
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments	Sep 22, 2022	ClusteringFederated Learning	—Unverified
One-Shot Federated Learning with Bayesian Pseudocoresets	Jun 4, 2024	Federated Learning	—Unverified
One-Shot Federated Learning with Classifier-Guided Diffusion Models	Nov 15, 2023	Federated Learning	—Unverified
One-Shot Federated Learning with Classifier-Free Diffusion Models	Feb 12, 2025	BenchmarkingDataset Generation	—Unverified
One-Shot Federated Learning with Neuromorphic Processors	Nov 1, 2020	Federated LearningGesture Recognition	—Unverified
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling	Mar 13, 2025	Domain AdaptationFederated Learning	—Unverified
On Feasibility of Server-side Backdoor Attacks on Split Learning	Feb 19, 2023	Backdoor AttackFederated Learning	—Unverified
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods	Sep 25, 2019	Federated LearningHyperparameter Optimization	—Unverified
On Federated Learning with Energy Harvesting Clients	Feb 12, 2022	Federated LearningScheduling	—Unverified
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment	May 29, 2025	Federated LearningPolicy Gradient Methods	—Unverified
On Heterogeneously Distributed Data, Sparsity Matters	Sep 29, 2021	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