Challenges and future directions of secure federated learning: a survey

Zhang, Kaiyue1; Song, Xuan3,4; Zhang, Chenhan2; Yu, Shui2

2022-10-01

10.1007/s11704-021-0598-z

Frontiers of Computer Science   影响因子和分区

2095-2228

2095-2236

Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.

federated learning privacy protection security

SCI ; EI

英语

第一 ; 通讯

Guangdong Provincial Key Laboratory[2020B121201001]

Computer Science

Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000729093400001

HIGHER EDUCATION PRESS

20215011326391

Data privacy

2-s2.0-85120989981

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
2.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, SUSTech UTokyo Joint Res Ctr Super Smart City, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China

Zhang, Kaiyue,Song, Xuan,Zhang, Chenhan,et al. Challenges and future directions of secure federated learning: a survey[J]. Frontiers of Computer Science,2022,16(5).

Zhang, Kaiyue,Song, Xuan,Zhang, Chenhan,&Yu, Shui.(2022).Challenges and future directions of secure federated learning: a survey.Frontiers of Computer Science,16(5).

Zhang, Kaiyue,et al."Challenges and future directions of secure federated learning: a survey".Frontiers of Computer Science 16.5(2022).