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 901–925 of 6771 papers

Title	Date	Tasks	Status
A New Implementation of Federated Learning for Privacy and Security Enhancement	Aug 3, 2022	Federated Learning	—Unverified
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data	Mar 15, 2025	Demand ForecastingFeature Importance	—Unverified
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning	Feb 17, 2025	FairnessFederated Learning	—Unverified
A Closer Look at Personalization in Federated Image Classification	Apr 22, 2022	ClassificationEdge-computing	—Unverified
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity	Jul 31, 2024	Federated LearningSynthetic Data Generation	—Unverified
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation	Mar 18, 2022	Federated LearningImage Segmentation	—Unverified
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning	May 1, 2022	Dimensionality ReductionFederated Learning	—Unverified
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis	Mar 9, 2023	Contrastive LearningFederated Learning	—Unverified
Collaborative Distributed Machine Learning	Sep 28, 2023	Federated Learning	—Unverified
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks	Oct 9, 2023	Federated Learning	—Unverified
Abnormal Local Clustering in Federated Learning	Aug 26, 2022	ClusteringFederated Learning	—Unverified
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction	Feb 28, 2025	Federated LearningKolmogorov-Arnold Networks	—Unverified
Federated Learning in Mobile Edge Networks: A Comprehensive Survey	Sep 26, 2019	Edge-computingFederated Learning	—Unverified
An Enhanced Federated Prototype Learning Method under Domain Shift	Sep 27, 2024	Federated Learning	—Unverified
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning	Nov 16, 2020	Federated Learning	—Unverified
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies	Apr 2, 2025	Federated Learning	—Unverified
Budgeted Online Model Selection and Fine-Tuning via Federated Learning	Jan 19, 2024	Federated LearningModel Selection	—Unverified
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication	Sep 26, 2022	Federated Learning	—Unverified
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning	Jun 5, 2024	Federated Learning	—Unverified
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering	Mar 30, 2025	AllContrastive Learning	—Unverified
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning	Dec 17, 2024	Federated Learning	—Unverified
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates	Jun 13, 2025	Federated LearningReconstruction Attack	—Unverified
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets	Aug 18, 2024	Federated Learning	—Unverified
Byzantine-Resilient Federated Learning at Edge	Mar 18, 2023	Federated Learning	—Unverified
Decision Models for Selecting Federated Learning Architecture Patterns	Apr 28, 2022	Federated LearningManagement	—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