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 6301–6325 of 6771 papers

Title	Date	Tasks	Status
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing	Jan 9, 2025	ArticlesFederated Learning	CodeCode Available
Vertical Federated Unlearning via Backdoor Certification	Dec 16, 2024	Federated LearningVertical Federated Learning	CodeCode Available
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors	Nov 18, 2024	Federated LearningMissing Values	CodeCode Available
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit	May 6, 2023	Federated LearningScene Classification	CodeCode Available
Frequency-Based Federated Domain Generalization for Polyp Segmentation	Oct 2, 2024	Domain GeneralizationFederated Learning	CodeCode Available
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness	Oct 18, 2023	FairnessFederated Learning	CodeCode Available
Fair Resource Allocation in Federated Learning	May 25, 2019	FairnessFederated Learning	CodeCode Available
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning	Apr 27, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation	Mar 25, 2023	Federated LearningFew-Shot Learning	CodeCode Available
Real-World Image Datasets for Federated Learning	Oct 14, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available
Three Approaches for Personalization with Applications to Federated Learning	Feb 25, 2020	AllBIG-bench Machine Learning	CodeCode Available
A Survey on Contribution Evaluation in Vertical Federated Learning	May 3, 2024	Federated LearningSurvey	CodeCode Available
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration	Sep 29, 2023	Continual LearningFederated Learning	CodeCode Available
Security Assessment of Hierarchical Federated Deep Learning	Aug 20, 2024	Deep LearningFederated Learning	CodeCode Available
Model-Agnostic Federated Learning	Mar 8, 2023	Federated Learningmodel	CodeCode Available
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data	Feb 14, 2023	Federated Learning	CodeCode Available
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models	Jul 7, 2025	Federated LearningPersonalized Federated Learning	CodeCode Available
Ensemble Distillation for Robust Model Fusion in Federated Learning	Jun 12, 2020	BIG-bench Machine LearningFederated Learning	CodeCode Available
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data	Apr 15, 2023	Federated LearningImage Reconstruction	CodeCode Available
Enhanced Security and Privacy via Fragmented Federated Learning	Jul 13, 2022	Federated Learning	CodeCode Available
Clustered Federated Learning via Embedding Distributions	Jun 9, 2025	ClusteringDomain Adaptation	CodeCode Available
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning	Feb 24, 2023	Federated Learning	CodeCode Available
Federated Learning From Big Data Over Networks	Oct 27, 2020	Federated Learningregression	CodeCode Available
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints	Oct 4, 2019	ClusteringFederated Learning	CodeCode Available
Model Fusion with Kullback--Leibler Divergence	Jul 13, 2020	Federated Learningmodel	CodeCode Available

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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