LayerCFL:an efcient federated learning with layer-wised clustering  

作　　者：Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li 

机构地区：[1]School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing,China [2]Key Laboratory of Trustworthy Distributed Computing and Service(BUPT),Ministry of Education,Beijing,China [3]Institute of Computer Science,Faculty of Mathematics and Computer Science,University of Goettingen,Göettingen,Germany [4]School of Information Technology,Henan University of Chinese Medicine,Zhengzhou,Henan,China

出　　处：《Cybersecurity》2025年第1期72-85,共14页网络空间安全科学与技术(英文)

基　　金：Supported by the National Natural Science Foundation of China(No.62002028,No.62102040 and No.62202066).

摘　　要：Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model training.To address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them separately.Despite its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown beforehand.This is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized models.To tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering technique.In LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental methods.Our experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关 键 词：Federated learning Clustered federated learning Non-IID Layer-wised clustering 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]