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 2326–2350 of 6771 papers

Title	Date	Tasks	Status
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models	Mar 22, 2024	Federated LearningUncertainty Quantification	—Unverified
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process	Jun 13, 2022	Federated LearningKnowledge Distillation	—Unverified
Communication-Efficient Robust Federated Learning with Noisy Labels	Jun 11, 2022	Bilevel OptimizationFederated Learning	—Unverified
A study on performance limitations in Federated Learning	Jan 7, 2025	Federated Learning	—Unverified
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets	Jun 17, 2020	Federated Learning	—Unverified
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example	Oct 30, 2024	Federated LearningVertical Federated Learning	—Unverified
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges	Aug 27, 2022	Federated LearningPrivacy Preserving	—Unverified
Federated Automatic Differentiation	Jan 18, 2023	FADFederated Learning	—Unverified
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks	Jan 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data	Sep 6, 2024	Federated LearningVertical Federated Learning	—Unverified
Federated Anomaly Detection over Distributed Data Streams	May 16, 2022	Anomaly DetectionFederated Learning	—Unverified
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data	Nov 28, 2018	BIG-bench Machine LearningFederated Learning	—Unverified
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection	Jan 30, 2024	Federated Learning	—Unverified
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks	May 3, 2024	Federated Learning	—Unverified
Federated Automated Feature Engineering	Dec 5, 2024	Automated Feature EngineeringFeature Engineering	—Unverified
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes	Jul 24, 2024	Federated LearningGaussian Processes	—Unverified
Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments	Jul 8, 2025	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory	Mar 9, 2020	Federated Learning	—Unverified
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning	Jan 1, 2021	Federated Learning	—Unverified
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning	Apr 15, 2024	Federated LearningVertical Federated Learning	—Unverified
A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication	Jul 7, 2025	Federated Learning	—Unverified
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients	Mar 25, 2024	Federated Learning	—Unverified
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework	Mar 15, 2025	Federated LearningState Space Models	—Unverified
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning	Aug 3, 2024	Federated Learning	—Unverified
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission	Aug 7, 2023	Federated LearningSemantic Communication	—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