A survey on cross-user federated recommendation  

作　　者：Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING 

机构地区：[1]College of Computer Science and Software Engineering,Shenzhen University,Shenzhen 518060,China [2]School of Electrical Engineering and Computer Science,The University of Queensland,Brisbane QLD 4072,Australia [3]WeBank AI Lab,WeBank,Shenzhen 518052,China [4]Department of Computer Science and Engineering,Hong Kong University of Science and Technology,Hong Kong 999077,China [5]College of Big Data and Internet,Shenzhen Technology University,Shenzhen 518118,China [6]Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen 518123,China

出　　处：《Science China(Information Sciences)》2025年第4期3-28,共26页中国科学(信息科学)(英文版)

基　　金：supported by Basic Research Fund in Shenzhen Natural Science Foundation(Grant No.JCYJ20240813141441054);National Natural Science Foundation of China(Grant Nos.62461160311,62272315);National Key Research and Development Program of China(Grant No.2023YFF0725100)。

摘　　要：Recommender systems are effective in mitigating information overload,yet the centralized storage of user data raises significant privacy concerns.Cross-user federated recommendation(CUFR)provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices.In this survey,we review and categorize current progress in CUFR,focusing on four key aspects:privacy,security,accuracy,and efficiency.Firstly,we conduct an in-depth privacy analysis,discuss various cases of privacy leakage,and then review recent methods for privacy protection.Secondly,we analyze security concerns and review recent methods for untargeted and targeted attacks.For untargeted attack methods,we categorize them into data poisoning attack methods and parameter poisoning attack methods.For targeted attack methods,we categorize them into user-based methods and item-based methods.Thirdly,we provide an overview of the federated variants of some representative methods,and then review the recent methods for improving accuracy from two categories:data heterogeneity and high-order information.Fourthly,we review recent methods for improving training efficiency from two categories:client sampling and model compression.Finally,we conclude this survey and explore some potential future research topics in CUFR.

关 键 词：cross-user federated recommendation federated recommendation federated learning recommender systems user privacy 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]