Decentralized Federated Learning with Prototype Exchange

Lu Qi, Haoze Chen (), Hongliang Zou, Shaohua Chen, Xiaoying Zhang and Hongyan Chen
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Lu Qi: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Haoze Chen: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Hongliang Zou: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Shaohua Chen: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Xiaoying Zhang: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China
Hongyan Chen: College of Modern Science and Technology, China Jiliang University, Yiwu 322002, China

Mathematics, 2025, vol. 13, issue 2, 1-20

Abstract: As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. To address these issues, we propose PearFL, a decentralized FL framework that enhances collaboration and model generalization by introducing prototype exchange mechanisms. PearFL allows each client to share lightweight prototype information with its neighbors, minimizing communication overhead and improving model consistency across distributed devices. Experimental evaluations on benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100, demonstrate that PearFL achieves superior communication efficiency, convergence speed, and accuracy compared to conventional FL methods. These results confirm PearFL’s efficacy as a scalable solution for decentralized learning in heterogeneous and resource-constrained environments.

Keywords: federated learning; distributed machine learning; prototype exchange (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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