Article Details

Title · Authors · Abstract · Keywords · DOI · References

Research Review on Privacy Protection and Security Defense Strategies in Federated Learning

YUAN Liu, ZHANG Yong, JIAO Pengju, ZHONG Yang, TENG Shengbo

(China Mobile (Suzhou) Software Technology Co., Ltd., Suzhou 215000, China)

Section：Network and Information Security · Vol. 2026, Issue 17 · Pages：120-128,136 · 2096-4706（2026）17-0120-10 · CLC number：TP18 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.17.022

Abstract

As a distributed Machine Learning method, Federated Learning realizes joint training over distributed data through encrypted parameter exchange while keeping data stored locally, taking into account both data sharing and privacy protection. It is now widely used in the Internet of Things, industrial Internet of Things, smart healthcare, power systems, and other fields. However, Federated Learning still faces potential risks of privacy leakage and data security issues. This paper systematically sorts out the privacy and security problems in Federated Learning, and analyzes various malicious attack methods and security defense methods, so as to help mitigate the potential risks in constructing federated learning computing environments. In addition, this paper discusses the future research directions of Federated Learning, aiming to provide references for further promoting its practical application in complex scenarios.

Keywords

Federated Learning; privacy protection; data security; Machine Learning

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About the authors

Yuan Liu (January 1990-), male, Han ethnicity, from Suqian, Jiangsu, intermediate engineer, master's student, research interests include cloud computing and artificial intelligence.