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 251–275 of 6771 papers

Title	Date	Tasks	Status
Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios	May 16, 2025	Federated LearningVertical Federated Learning	—Unverified
Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI	May 16, 2025	Federated Learning	—Unverified
FedDuA: Doubly Adaptive Federated Learning	May 16, 2025	Federated Learning	—Unverified
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations	May 15, 2025	Federated LearningGraph Learning	—Unverified
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network	May 15, 2025	Autonomous VehiclesFederated Learning	—Unverified
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning	May 15, 2025	Federated LearningReconstruction Attack	—Unverified
Sybil-based Virtual Data Poisoning Attacks in Federated Learning	May 15, 2025	Data PoisoningFederated Learning	—Unverified
Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning	May 15, 2025	Federated Learning	—Unverified
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning	May 15, 2025	Backdoor AttackFederated Learning	—Unverified
Robust Federated Learning on Edge Devices with Domain Heterogeneity	May 15, 2025	Contrastive LearningDiversity	—Unverified
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence	May 14, 2025	Deep LearningFederated Learning	—Unverified
Energy-Efficient Federated Learning for AIoT using Clustering Methods	May 14, 2025	ClusteringFederated Learning	CodeCode Available
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data	May 14, 2025	Federated LearningMissing Labels	—Unverified
Toward Malicious Clients Detection in Federated Learning	May 14, 2025	Federated Learning	—Unverified
Toward Fair Federated Learning under Demographic Disparities and Data Imbalance	May 14, 2025	FairnessFederated Learning	CodeCode Available
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features	May 14, 2025	Federated Learning	CodeCode Available
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions	May 13, 2025	Federated LearningFew-Shot Learning	—Unverified
Multi-Layer Hierarchical Federated Learning with Quantization	May 13, 2025	Federated LearningQuantization	—Unverified
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations	May 13, 2025	EconometricsFederated Learning	—Unverified
Modular Federated Learning: A Meta-Framework Perspective	May 13, 2025	Federated LearningSurvey	—Unverified
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing	May 13, 2025	Federated Learning	—Unverified
Securing Genomic Data Against Inference Attacks in Federated Learning Environments	May 12, 2025	Federated LearningInference Attack	—Unverified
A Federated Random Forest Solution for Secure Distributed Machine Learning	May 12, 2025	Federated LearningIncremental Learning	CodeCode Available
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices	May 12, 2025	Federated Learning	—Unverified
Sharp Gaussian approximations for Decentralized Federated Learning	May 12, 2025	Federated Learningvalid	—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