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 1026–1050 of 6771 papers

Title	Date	Tasks	Status
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning	Jan 10, 2024	Federated Learningimage-classification	—Unverified
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11	Mar 6, 2025	Federated Learning	—Unverified
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms	Dec 24, 2023	Federated LearningPrivacy Preserving	—Unverified
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning	May 16, 2019	Brain SegmentationFederated Learning	—Unverified
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification	Apr 24, 2024	Federated Learningimage-classification	—Unverified
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks	Aug 9, 2023	Federated Learning	—Unverified
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning	Feb 7, 2022	Federated LearningSurvey	—Unverified
Abnormal Client Behavior Detection in Federated Learning	Oct 22, 2019	Anomaly DetectionFederated Learning	—Unverified
Communication-Efficient Federated Learning with Sketching	Jan 1, 2020	Federated Learning	—Unverified
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching	Oct 30, 2024	Federated Learning	—Unverified
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission	Aug 7, 2023	Federated LearningSemantic Communication	—Unverified
CommunityAI: Towards Community-based Federated Learning	Nov 29, 2023	Federated Learning	—Unverified
Brain MRI Screening Tool with Federated Learning	Nov 23, 2023	Federated Learning	—Unverified
An Empirical Evaluation of Federated Contextual Bandit Algorithms	Mar 17, 2023	Federated LearningMulti-Armed Bandits	—Unverified
Achieving Personalized Federated Learning with Sparse Local Models	Jan 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare	Jul 24, 2022	Federated LearningPrivacy Preserving	—Unverified
Boosting with Multiple Sources	Dec 1, 2021	Federated Learning	—Unverified
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack	Dec 24, 2024	Adversarial AttackFederated Learning	—Unverified
Communication-Efficient Federated Learning for Neural Machine Translation	Dec 12, 2021	Federated LearningMachine Translation	—Unverified
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration	Oct 9, 2024	Federated Learning	—Unverified
Adaptive Gradient Clipping for Robust Federated Learning	May 23, 2024	ARCFederated Learning	—Unverified
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers	Apr 1, 2024	Federated LearningLoad Forecasting	—Unverified
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations	Apr 11, 2025	Federated LearningMedical Image Analysis	—Unverified
Boosting Federated Learning Convergence with Prototype Regularization	Jul 20, 2023	Federated Learning	—Unverified
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning	Jun 21, 2023	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