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 3126–3150 of 6771 papers

Title	Date	Tasks	Status
Federated Split BERT for Heterogeneous Text Classification	May 26, 2022	ClassificationFederated Learning	—Unverified
Federated Split Learning for Human Activity Recognition with Differential Privacy	Nov 9, 2024	Activity RecognitionFederated Learning	—Unverified
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks	Dec 9, 2024	Federated LearningQuantization	—Unverified
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	Dec 1, 2021	COVID-19 DiagnosisFederated Learning	—Unverified
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	May 21, 2021	COVID-19 DiagnosisFederated Learning	—Unverified
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training	Nov 2, 2021	COVID-19 DiagnosisFederated Learning	—Unverified
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning	Jul 23, 2021	Federated LearningScheduling	—Unverified
Federated Learning for Heterogeneous Bandits with Unobserved Contexts	Mar 29, 2023	Federated LearningMulti-Armed Bandits	—Unverified
Federated Stochastic Gradient Descent Begets Self-Induced Momentum	Feb 17, 2022	Federated Learning	—Unverified
A review of Federated Learning in Intrusion Detection Systems for IoT	Apr 26, 2022	Federated LearningIntrusion Detection	—Unverified
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining	Apr 8, 2025	Federated Learning	—Unverified
Device Scheduling with Fast Convergence for Wireless Federated Learning	Nov 3, 2019	Federated LearningScheduling	—Unverified
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression	Jan 23, 2023	Dimensionality ReductionFederated Learning	—Unverified
Federated Survival Analysis with Discrete-Time Cox Models	Jun 16, 2020	Federated LearningPrivacy Preserving	—Unverified
Bayesian data fusion with shared priors	Dec 14, 2022	Bayesian InferenceFederated Learning	—Unverified
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling	May 14, 2023	Distributed OptimizationFederated Learning	—Unverified
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning	Jan 19, 2025	Federated LearningPrivacy Preserving	—Unverified
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation	Oct 25, 2021	Face Presentation Attack DetectionFace Recognition	—Unverified
Federated Time Series Generation on Feature and Temporally Misaligned Data	Oct 28, 2024	Federated LearningTime Series	—Unverified
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets	Sep 30, 2022	Federated LearningRepresentation Learning	—Unverified
Federated Transfer Learning Aided Interference Classification in GNSS Signals	Jun 23, 2024	ClassificationFederated Learning	—Unverified
Federated Transfer Learning: concept and applications	Sep 26, 2020	Federated LearningTransfer Learning	—Unverified
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning	Feb 27, 2024	Federated Learning	—Unverified
Federated Transfer Learning with Differential Privacy	Mar 17, 2024	Federated Learningregression	—Unverified
A Review of Federated Learning in Energy Systems	Aug 20, 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