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 4476–4500 of 6771 papers

Title	Date	Tasks	Status
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning	Jan 28, 2024	Binary ClassificationEdge-computing	—Unverified
StochaLM: a Stochastic alternate Linearization Method for distributed optimization	Apr 23, 2020	Distributed OptimizationFederated Learning	—Unverified
Stochastic Approximation Approach to Federated Machine Learning	Feb 20, 2024	Federated Learning	—Unverified
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving	Oct 23, 2019	Federated LearningPrivacy Preserving	—Unverified
Stochastic Client Selection for Federated Learning with Volatile Clients	Nov 17, 2020	FairnessFederated Learning	—Unverified
Stochastic Clustered Federated Learning	Mar 2, 2023	Federated Learning	—Unverified
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design	Nov 8, 2022	Federated LearningPrivacy Preserving	—Unverified
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees	Jan 25, 2022	Federated LearningPrivacy Preserving	—Unverified
Stochastic Communication Avoidance for Recommendation Systems	Nov 3, 2024	Federated LearningGPU	—Unverified
Stochastic Controlled Averaging for Federated Learning with Communication Compression	Aug 16, 2023	Federated Learning	—Unverified
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees	Feb 25, 2020	Federated Learningparameter estimation	—Unverified
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization	Nov 2, 2023	Federated Learning	—Unverified
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity	Dec 28, 2020	Federated Learning	—Unverified
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network	Nov 16, 2023	Federated Learning	—Unverified
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients	Feb 12, 2021	Federated Learning	—Unverified
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks	Jan 10, 2024	AttributeDiversity	—Unverified
Stratified cross-validation for unbiased and privacy-preserving federated learning	Jan 22, 2020	Federated LearningPrivacy Preserving	—Unverified
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling	Apr 18, 2025	Federated Learning	—Unverified
Streaming Federated Learning with Markovian Data	Mar 24, 2025	Federated Learning	—Unverified
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient	Nov 28, 2024	Backdoor AttackFederated Learning	—Unverified
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization	May 13, 2024	Distributed OptimizationFederated Learning	—Unverified
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors	Jan 31, 2022	Anomaly DetectionBinary Classification	—Unverified
Study of the performance and scalability of federated learning for medical imaging with intermittent clients	Jul 18, 2022	Federated LearningMedical Image Analysis	—Unverified
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning	Sep 28, 2024	Federated LearningInference Attack	—Unverified
Subject Granular Differential Privacy in Federated Learning	Jun 7, 2022	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