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 2126–2150 of 6771 papers

Title	Date	Tasks	Status
Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective	Aug 28, 2024	Federated Learning	—Unverified
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs	Dec 19, 2023	Dimensionality ReductionDistributed Optimization	—Unverified
Convergence Time Optimization for Federated Learning over Wireless Networks	Jan 22, 2020	Federated Learning	—Unverified
Convergence Theory of Flexible ALADIN for Distributed Optimization	Mar 26, 2025	Distributed OptimizationFederated Learning	—Unverified
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning	Apr 22, 2020	Federated Learning	—Unverified
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers	Jun 3, 2024	Federated LearningForm	—Unverified
Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings	Jun 10, 2025	Federated Learning	—Unverified
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification	Apr 11, 2024	Federated LearningMulti-Task Learning	—Unverified
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices	Jan 6, 2024	Electromyography (EMG)Federated Learning	—Unverified
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning	Jun 17, 2025	FairnessFederated Learning	—Unverified
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy	Feb 2, 2023	Federated Learning	—Unverified
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes	Jan 17, 2023	Federated LearningMeta-Learning	—Unverified
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation	Oct 20, 2020	Federated LearningKnowledge Distillation	—Unverified
Convergence of Federated Learning over a Noisy Downlink	Aug 25, 2020	Federated LearningQuantization	—Unverified
Convergence of Agnostic Federated Averaging	Jul 14, 2025	Federated Learning	—Unverified
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates	Feb 8, 2024	Federated Learning	—Unverified
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting	Sep 29, 2024	Federated LearningGraph Learning	—Unverified
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning	Mar 8, 2021	Federated LearningMeta-Learning	—Unverified
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging	Mar 12, 2023	Brain Tumor SegmentationFederated Learning	—Unverified
A Framework for Double-Blind Federated Adaptation of Foundation Models	Feb 3, 2025	Federated Learningimage-classification	—Unverified
Convergence Analysis of Sequential Split Learning on Heterogeneous Data	Feb 3, 2023	Federated Learning	—Unverified
Convergence Analysis of Split Federated Learning on Heterogeneous Data	Feb 23, 2024	Federated Learning	—Unverified
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations	Feb 28, 2022	Fault DiagnosisFederated Learning	—Unverified
Asynchronous Collaborative Learning Across Data Silos	Mar 23, 2022	BIG-bench Machine LearningFederated Learning	—Unverified
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping	May 18, 2023	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