计算机科学
聚类分析
可扩展性
联合学习
初始化
云计算
同种类的
数据挖掘
异构网络
协同过滤
分布式计算
机器学习
推荐系统
数据库
无线
电信
物理
无线网络
热力学
程序设计语言
操作系统
作者
Liwen Zhang,Zongben Xu
标识
DOI:10.1109/icetci57876.2023.10176651
摘要
Federated Learning (FL) has attracted much attention in recent years as a promising framework of privacy-preserving and scalability. However, the framework is highly vulnerable to data heterogeneity, which widely exists in real-world applications. In this paper, we designed a communication-efficient personalized federated clustering algorithm k-PFed, which overcomes the data heterogeneity and various network scenarios in the FL system. By initializing local datasets by pre-clustering, clients in k-PFed are able to generate a personalized model locally. In this manner, the local and the global model in k-PFed can be trained independently. Meanwhile, the k-PFed designed three working modes for offline, P2P, and cloud-collaborative networks. Experiments on homogeneous and heterogeneous data show that the clustering accuracy is significantly better than other federated clustering algorithms, and the decrease caused by data heterogeneity on k-PFed is significantly lower than that of other algorithms.
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