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 5126–5150 of 6771 papers

Title	Date	Tasks	Status
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers	Jun 8, 2023	Adversarial AttackFederated Learning	—Unverified
Mitigating federated learning contribution allocation instability through randomized aggregation	May 13, 2024	FairnessFederated Learning	—Unverified
Mitigating Group Bias in Federated Learning for Heterogeneous Devices	Sep 13, 2023	Decision MakingEmotion Recognition	—Unverified
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models	May 3, 2025	counterfactualFairness	—Unverified
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity	Apr 5, 2024	cross-modal alignmentFederated Learning	—Unverified
Mitigating Leakage in Federated Learning with Trusted Hardware	Nov 10, 2020	Federated Learning	—Unverified
Mitigating Membership Inference Vulnerability in Personalized Federated Learning	Mar 12, 2025	ClusteringFairness	—Unverified
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training	Aug 18, 2024	Federated Learning	—Unverified
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity	Jun 3, 2025	Federated Learning	—Unverified
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems	Jan 24, 2024	Federated Learning	—Unverified
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup	Jun 17, 2020	Federated LearningPrivacy Preserving	—Unverified
Mixed Federated Learning: Joint Decentralized and Centralized Learning	May 26, 2022	Federated Learning	—Unverified
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation	Jun 4, 2024	Computational EfficiencyEdge-computing	—Unverified
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices	Nov 29, 2023	BenchmarkingFederated Learning	—Unverified
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning	Oct 22, 2022	Federated LearningQuantization	—Unverified
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers	Sep 26, 2021	AttributeFederated Learning	—Unverified
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation	Jun 29, 2023	Domain GeneralizationFederated Learning	—Unverified
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning	Sep 9, 2024	Federated LearningImage Captioning	—Unverified
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning	May 11, 2025	Federated LearningPortfolio Optimization	—Unverified
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles	Dec 7, 2022	Federated LearningTrajectory Prediction	—Unverified
Mobile Application for Oral Disease Detection using Federated Learning	Oct 27, 2023	Federated Learningobject-detection	—Unverified
Mobile Augmented Reality with Federated Learning in the Metaverse	Dec 16, 2022	Federated Learningobject-detection	—Unverified
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices	Jun 26, 2019	Edge-computingFederated Learning	—Unverified
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks	Jan 18, 2024	Federated LearningPrivacy Preserving	—Unverified
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification	Jun 8, 2025	Federated Learningimage-classification	—Unverified

Show:10 25 50

← PrevPage 206 of 271Next →

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