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 1076–1100 of 6771 papers

Title	Date	Tasks	Status	Score
Federated Multi-Task Learning	May 30, 2017	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises	Mar 3, 2025	Federated LearningPrivacy Preserving	CodeCode Available	5
Federated Low-Rank Adaptation for Foundation Models: A Survey	May 16, 2025	Federated LearningSurvey	CodeCode Available	5
Federated LoRA with Sparse Communication	Jun 7, 2024	Federated Learning	CodeCode Available	5
Federated Machine Learning: Concept and Applications	Feb 13, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces	Dec 2, 2024	Brain Computer InterfaceEEG	CodeCode Available	5
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis	Apr 26, 2023	ClusteringFault Diagnosis	CodeCode Available	5
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design	May 10, 2021	Federated Learning	CodeCode Available	5
Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	Feb 8, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available	5
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery	Aug 6, 2024	DisentanglementFederated Learning	CodeCode Available	5
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!	Jun 9, 2023	Federated Learning	CodeCode Available	5
Federated Two Stage Decoupling With Adaptive Personalization Layers	Aug 30, 2023	ClusteringFederated Learning	CodeCode Available	5
FedIMPUTE: Privacy-Preserving Missing Value Imputation for Multi-Site Heterogeneous Electronic Health Records	Aug 22, 2024	Federated LearningMissing Values	CodeCode Available	5
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation	Jul 17, 2024	Federated LearningPrediction	CodeCode Available	5
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels	Aug 19, 2022	Federated LearningLearning with noisy labels	CodeCode Available	5
Federated Learning with Intermediate Representation Regularization	Oct 28, 2022	Federated Learning	CodeCode Available	5
Federated Learning with Non-IID Data	Jun 2, 2018	Federated Learning	CodeCode Available	5
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks	Sep 17, 2019	Federated Learning	CodeCode Available	5
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities	May 20, 2024	Computational EfficiencyFederated Learning	CodeCode Available	5
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks	Apr 22, 2024	Federated Learning	CodeCode Available	5
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection	May 2, 2024	Autonomous DrivingFederated Learning	CodeCode Available	5
Federated Learning With Individualized Privacy Through Client Sampling	Jan 29, 2025	Federated Learning	CodeCode Available	5
Federated Learning with Only Positive Labels	Apr 21, 2020	Federated LearningMulti-class Classification	CodeCode Available	5
BAFFLE : Blockchain Based Aggregator Free Federated Learning	Sep 16, 2019	Computational EfficiencyFederated Learning	CodeCode Available	5
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning	Jul 6, 2024	Federated Learningimage-classification	CodeCode Available	5

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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