现代信息科技2024，Vol.8Issue(13)：61-64,69,5.DOI:10.19850/j.cnki.2096-4706.2024.13.013
基于动态聚类的个性化联邦学习与模块化组合策略
Personalized Federated Learning Based on Dynamic Clustering and Modular Combinatorial Strategy
摘要
Abstract
This paper proposes a personalized federated learning method based on dynamic clustering to address the issue of heterogeneous data in Federated Learning.This method combines the optimization target vector with the agglomerative clustering algorithm,dynamically divides clients with significant data differences into different clusters while conserving computing resources.Furthermore,in consideration of the sustainability of training models,the paper further proposes a modular combinatorial strategy,where new clients only need to combine previously trained models to obtain an initial model suitable for local tasks.The client only needs to perform a small amount of training on this initial model to apply it to local tasks.On the Cafir-10 and Minst datasets,the model's accuracy is superior to that of locally retrained models.关键词
联邦学习/个性化/深度神经网络/可组合/动态聚类Key words
Federated Learning/personalization/Deep Neural Network/combinatorial/dynamic clustering分类
信息技术与安全科学引用本文复制引用
周洪炜,马源,马旭..基于动态聚类的个性化联邦学习与模块化组合策略[J].现代信息科技,2024,8(13):61-64,69,5.
