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 1751–1775 of 6771 papers

Title	Date	Tasks	Status	Score
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability	Sep 26, 2024	Federated Learning	CodeCode Available	5
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations	Jul 25, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images	Aug 28, 2022	Federated Learning	CodeCode Available	5
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation	Jul 3, 2024	Federated Learning	CodeCode Available	5
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge	Apr 23, 2018	Edge-computingFederated Learning	CodeCode Available	5
Efficient and Robust Regularized Federated Recommendation	Nov 3, 2024	Federated LearningRecommendation Systems	CodeCode Available	5
Early prediction of the risk of ICU mortality with Deep Federated Learning	Dec 1, 2022	Federated LearningICU Mortality	CodeCode Available	5
Security Assessment of Hierarchical Federated Deep Learning	Aug 20, 2024	Deep LearningFederated Learning	CodeCode Available	5
Towards Energy-Aware Federated Learning on Battery-Powered Clients	Aug 9, 2022	FairnessFederated Learning	CodeCode Available	5
Client Recruitment for Federated Learning in ICU Length of Stay Prediction	Apr 28, 2023	Federated LearningLength-of-Stay prediction	CodeCode Available	5
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning	Jun 16, 2025	Computational EfficiencyFederated Learning	CodeCode Available	5
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing	Feb 5, 2025	Federated LearningScheduling	CodeCode Available	5
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation	Sep 8, 2024	Federated Learning	CodeCode Available	5
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning	Oct 2, 2024	FairnessFederated Learning	CodeCode Available	5
Dynamically Weighted Federated k-Means	Oct 23, 2023	ClusteringFederated Learning	CodeCode Available	5
BOBA: Byzantine-Robust Federated Learning with Label Skewness	Aug 27, 2022	Federated LearningSelection bias	CodeCode Available	5
Client-Edge-Cloud Hierarchical Federated Learning	May 16, 2019	Federated Learning	CodeCode Available	5
FDAPT: Federated Domain-adaptive Pre-training for Language Models	Jul 12, 2023	Computational EfficiencyFederated Learning	CodeCode Available	5
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation	Jun 13, 2025	Federated Learning	CodeCode Available	5
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets	Nov 9, 2018	Federated Learning	CodeCode Available	5
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning	Nov 11, 2020	Federated LearningRecommendation Systems	—Unverified	0
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX	May 4, 2021	Federated Learning	—Unverified	0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data	Jan 19, 2025	Data PoisoningFederated Learning	—Unverified	0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI	Mar 19, 2025	Deep LearningFederated Learning	—Unverified	0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning	Feb 23, 2024	Federated Learning	—Unverified	0

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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