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 2976–3000 of 6771 papers

Title	Date	Tasks	Status
Federated Learning with Projected Trajectory Regularization	Dec 22, 2023	Federated Learning	—Unverified
Federated Learning With Quantized Global Model Updates	Jun 18, 2020	Federated Learningmodel	—Unverified
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case	May 3, 2024	Federated Learning	—Unverified
Federated Learning for Spoken Language Understanding	Dec 1, 2020	Federated LearningIntent Detection	—Unverified
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies	Jan 29, 2024	DiagnosticFederated Learning	—Unverified
Federated Learning with Regularized Client Participation	Feb 7, 2023	Federated Learning	—Unverified
Federated Learning with Relative Fairness	Nov 2, 2024	Computational EfficiencyFairness	—Unverified
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology	Jun 11, 2022	Deep LearningFederated Learning	—Unverified
Federated Learning for Sparse Principal Component Analysis	Nov 15, 2023	Computational EfficiencyFederated Learning	—Unverified
Federated Learning for Smart Healthcare: A Survey	Nov 16, 2021	Federated LearningManagement	—Unverified
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data	Oct 6, 2022	Auxiliary LearningFederated Learning	—Unverified
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey	Dec 14, 2023	Data PoisoningFederated Learning	—Unverified
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach	Sep 14, 2022	Federated Learning	—Unverified
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities	Sep 16, 2024	Federated LearningSurvey	—Unverified
Federated Learning for Short Text Clustering	Nov 23, 2023	ClusteringFederated Learning	—Unverified
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization	Nov 27, 2024	Federated Learning	—Unverified
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach	Nov 30, 2023	Federated LearningModel Poisoning	—Unverified
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions	May 21, 2025	Federated LearningPersonalized Federated Learning	—Unverified
Federated Learning for Short-term Residential Load Forecasting	May 27, 2021	Computational EfficiencyFederated Learning	—Unverified
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection	Dec 30, 2024	Federated LearningPrivacy Preserving	—Unverified
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization	Feb 2, 2022	Distributed OptimizationFederated Learning	—Unverified
Federated Linear Contextual Bandits with Heterogeneous Clients	Feb 29, 2024	AllFederated Learning	—Unverified
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages	May 18, 2023	DiagnosticFederated Learning	—Unverified
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks	Mar 14, 2025	Action DetectionActivity Detection	—Unverified
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art	Feb 25, 2020	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