Challenges and future directions of secure federated learning: a survey

• Published in: Frontiers of Computer Science Vol. 16; no. 5; p. 165817
• Main Authors: ZHANG, Kaiyue, SONG, Xuan, ZHANG, Chenhan, YU, Shui
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.10.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Big Data; Computer Science; Datasets; Deep learning; Engineering; Federated learning; Parameters; Privacy; privacy protection; Review; Review Article; security; State-of-the-art reviews; privacy protection; security; federated learning
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-021-0598-z

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.