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 3301–3325 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions	Oct 30, 2024	DiagnosticFederated Learning	—Unverified
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination	Mar 28, 2023	Federated LearningIntrusion Detection	—Unverified
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning	Aug 19, 2021	Federated Learning	—Unverified
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives	Oct 10, 2024	Federated Learning	—Unverified
Cross-Silo Federated Learning: Challenges and Opportunities	Jun 26, 2022	Federated Learning	—Unverified
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy	Aug 17, 2024	Federated LearningModel Optimization	—Unverified
A Systematic Literature Review on Client Selection in Federated Learning	Jun 8, 2023	FairnessFederated Learning	—Unverified
Federated Learning for Data and Model Heterogeneity in Medical Imaging	Jul 31, 2023	Federated LearningKnowledge Distillation	—Unverified
Federated Learning for Cyber Physical Systems: A Comprehensive Survey	May 8, 2025	Federated LearningSurvey	—Unverified
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration	Mar 31, 2025	Federated Learning	—Unverified
Federated Learning for Cross-block Oil-water Layer Identification	Dec 29, 2021	Federated Learning	—Unverified
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles	Mar 10, 2022	Federated LearningSurvey	—Unverified
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration	Dec 19, 2024	Computational EfficiencyData Integration	—Unverified
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses	Nov 5, 2024	Federated LearningPrivacy Preserving	—Unverified
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data	Sep 18, 2023	Federated LearningScheduling	—Unverified
Layer-wise and Dimension-wise Locally Adaptive Federated Learning	Oct 1, 2021	Federated Learning	—Unverified
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement	Nov 7, 2024	Federated Learning	—Unverified
Cross-Modal Vertical Federated Learning for MRI Reconstruction	Jun 5, 2023	DisentanglementFederated Learning	—Unverified
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning	Dec 23, 2024	Federated LearningKnowledge Distillation	—Unverified
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks	Feb 14, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks	May 7, 2024	Collaborative InferenceFederated Learning	—Unverified
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges	Aug 21, 2023	Federated Learning	—Unverified
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality	Jan 25, 2024	cross-modal alignmentFederated Learning	—Unverified
Federated Learning for Computer Vision	Aug 24, 2023	Federated Learning	—Unverified
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey	Jul 18, 2023	Federated LearningPrivacy Preserving	—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