FedCPS:A Dual Optimization Model for Federated Learning Based on Clustering and Personalization Strategy  

作　　者：Zhen Yang Yifan Liu Fan Feng Yi Liu Zhenpeng Liu 

机构地区：[1]School of Cyber Security and Computer,Hebei University,Baoding,071000,China [2]School of Management,Hebei University,Baoding,071000,China [3]Information Technology Center,Hebei University,Baoding,071000,China

出　　处：《Computers, Materials & Continua》2025年第4期357-380,共24页计算机、材料和连续体(英文)

基　　金：supported by the Foundation of President of Hebei University(XZJJ202303).

摘　　要：Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.

关 键 词：Federated learning CLUSTER PERSONALIZATION OVERFITTING 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]