Survey on Attack Methods and Defense Mechanisms in Federated Learning

被引:0
作者
Zhang, Shiwen [1 ]
Chen, Shuang [1 ]
Liang, Wei [1 ]
Li, Renfa [2 ]
机构
[1] School of Computer Science and Engineering, Hunan University of Science and Technology, Hunan, Xiangtan
[2] School of Computer Science and Electronic Engineering, Hunan University, Changsha
关键词
attack method; defense mechanism; federated learning; privacy protection;
D O I
10.3778/j.issn.1002-8331.2306-0243
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The attack and defense techniques of federated learning are the core issue of federated learning system security. The attack and defense techniques of federated learning can significantly reduce the risk of being attacked and greatly enhance the security of federated learning systems. Deeply understanding the attack and defense techniques of federated learning can advance research in the field and achieve its widespread application of federated learning. Therefore, it is of great significance to study the attack and defense techniques of federated learning. Firstly, this paper briefly introduces the concept, basic workflow, types, and potential existing security issues of federated learning. Subsequently, the paper introduces the attacks that the federated learning system may encounter, and relevant research is summarized during the introduction. Then, starting from whether the federated learning system has targeted defense measures, the defense measures are divided into two categories:universal defense measures and targeted defense measures, and targeted summary are made. Finally, it reviews and analyzes the future research directions for the security of federated learning, providing reference for relevant researchers in their research work on the security of federated learning. © 2024 The Author(s).
引用
收藏
页码:1 / 16
页数:15
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