FUSE:a federated learning and U-shape split learning-based electricity theft detection framework  

作　　者：Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN 

机构地区：[1]School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China [2]School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China [3]Department of Finance,Operations,and Information Systems FOIS,Brock University,St.Catharines L2S 3A1,Canada

出　　处：《Science China(Information Sciences)》2024年第4期335-336,共2页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.62372173)。

摘　　要：In recent years,the data-driven electricity theft detection methods integrated with edge cloud computing[1,2]have not only demonstrated superior detection accuracy but also improved efficiency,making them viable alternatives to indoor inspections.Energy service providers(ESPs)typically manage regions by dividing them into various transformer districts(TDs).The detection of electricity theft in a particular region is performed by the associated TD。

关 键 词：DISTRICT CLOUD dividing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM73[自动化与计算机技术—控制科学与工程]