A survey on federated learning:a perspective from multi-party computation  

作　　者：Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG 

机构地区：[1]Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics,Beihang University,Beijing 100191,China [2]State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing,Beihang University,Beijing 100191,China [3]Pengcheng Laboratory,Shenzhen 518055,China [4]Zhongguancun Laboratory,Beijing 100190,China [5]Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering,Renmin University of China,Beijing 100872,China

出　　处：《Frontiers of Computer Science》2024年第1期93-103,共11页中国计算机科学前沿（英文版）

基　　金：partially supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.U21A20516,62076017,and 62141605);the Funding of Advanced Innovation Center for Future Blockchain and Privacy Computing(No.ZF226G2201);the Beihang University Basic Research Funding(No.YWF-22-L-531);the Funding(No.22-TQ23-14-ZD-01-001)and WeBank Scholars Program.

摘　　要：Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets.To enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model training.This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training efficiency.We also pinpoint future directions to deploy federated learning to a wider range of applications.

关 键 词：sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP309[自动化与计算机技术—控制科学与工程]