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 4201–4225 of 6771 papers

Title	Date	Tasks	Status
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data	Jun 1, 2022	Federated Learning	—Unverified
WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning	Jun 25, 2025	Contribution AssessmentFederated Learning	—Unverified
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models	Mar 5, 2025	Federated LearningPersonalized Federated Learning	—Unverified
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication	Nov 10, 2022	Federated LearningTransfer Learning	—Unverified
WassFFed: Wasserstein Fair Federated Learning	Nov 11, 2024	FairnessFederated Learning	—Unverified
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring	Nov 14, 2022	Federated Learning	—Unverified
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning	Jun 11, 2025	Anomaly DetectionFederated Learning	—Unverified
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy	Oct 22, 2021	Federated Learning	—Unverified
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks	May 24, 2023	DiversityFederated Learning	—Unverified
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems	Apr 6, 2025	Federated LearningIntrusion Detection	—Unverified
Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data	Jun 28, 2021	Federated Learning	—Unverified
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging	Aug 22, 2024	Federated Learning	—Unverified
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective	Feb 17, 2023	Distributed ComputingFairness	—Unverified
WEST: Word Encoded Sequence Transducers	Nov 20, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling	May 1, 2024	Federated LearningScheduling	—Unverified
What Do We Mean by Generalization in Federated Learning?	Oct 27, 2021	Federated Learning	—Unverified
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study	Mar 24, 2022	COVID-19 DiagnosisFederated Learning	—Unverified
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing	Feb 17, 2022	ClassificationCPU	—Unverified
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System	Feb 20, 2021	Federated LearningPrivacy Preserving	—Unverified
When Decentralized Optimization Meets Federated Learning	Jun 5, 2023	Federated Learning	—Unverified
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network	Sep 22, 2020	Decision MakingDeep Reinforcement Learning	—Unverified
When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA	Jan 24, 2023	Federated Learningimage-classification	—Unverified
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities	Apr 9, 2025	Federated Learning	—Unverified
When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection	Aug 7, 2023	Federated LearningPrivacy Preserving	—Unverified
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices	May 8, 2024	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