Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1171-1176

Defense method on poisoning attack based on clients in federated learning

Liu Jinquan1
Zhang Zheng1
Chen Zidong2
Cao Sheng2
1. Data Security Group, CHN Energy Dadu River Big Data Services Co. , Ltd. , Chengdu 610041, China
2. School of Computer Science & Engineering(School of Cyber Security), University of Electronic Science & Technology of China, Chengdu 611731, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0340

Abstract

The distributed training structure of federated learning is vulnerable to poisoning attacks. Existing methods mainly design secure aggregation algorithms for central servers to defend against poisoning attacks, but require the central server to be trusted and the number of poisoned participants to be lower than normal participants. To address the above issues, this paper proposed a poison attack defense method based on federated learning participants, which transfered the execution of defense strategies to the participants of federated learning. Firstly, each participant independently constructed a differential loss function, calculated the output of the global and local models, and conducted error analysis to obtain the weight and amount of differential loss. Secondly, it performed adaptive training based on the local trained loss function and differential loss function. Finally, this approach selected models based on the performance analysis of local and global models to prevent severely poisoned global models from interfering with normal clients. Experiments on datasets such as MNIST and FashionMNIST show that the federated learning training accuracy based on this algorithm is superior to poison attack defense methods such as DnC. Even when the proportion of poisoned participants exceeds half, normal participants can still achieve defense against poison attacks.

Foundation Support

四川省重点研发计划资助项目(2021YFG0113,2023YFG0118)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0340
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Technology of Information Security
Pages: 1171-1176
Serial Number: 1001-3695(2024)04-031-1171-06

Publish History

[2023-11-01] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

刘金全, 张铮, 陈自东, 等. 一种基于联邦学习参与方的投毒攻击防御方法 [J]. 计算机应用研究, 2024, 41 (4): 1171-1176. (Liu Jinquan, Zhang Zheng, Chen Zidong, et al. Defense method on poisoning attack based on clients in federated learning [J]. Application Research of Computers, 2024, 41 (4): 1171-1176. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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