App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 6

Gradient-hiding secure clustering and privacy-preserving federated learning

Li Gonglia,b
Ma Jingwena
Fan Yuna
a. School of Computer & Information Engineering, b. Key Laboratory of Artificial Intelligence & Personalized Learning in Education of Henan Province, Henan Normal University, Xinxiang 453007, Henan, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0403

Abstract

Federated learning is a kind of advanced distributed machine learning algorithm, which realizes multi-party cooperative training while ensuring the user's control over the data. However, the existing federated learning algorithms have many problems in dealing with Non-IID data, gradient information leakage and dynamic user offline. To solve these problems, this paper proposes a gradient hidden safe clustering and privacy-protecting federated learning based on quaternion, zero sharing and secret sharing techniques. Firstly, using quaternion rotation technology to hide the first-round model gradient and achieve secure clustering stratification without altering the gradient feature distribution, so as to solve the performance degradation issue caused by Non-IID data. Secondly, this paper designs a chain zero sharing algorithm, using single strategy to protect the user model gradient mask; Then, the threshold secret sharing is used to improve the robustness against offline users. Multi-dimensional comparison with other existing algorithms shows that the accuracy of SCFL is improved by about 3.13%-16.03% under the Non-IID data distribution, and the overall running time is improved by about 3-6 times. In this work, the security of information transmission is guaranteed at any stage, satisfying the design goals of accuracy, security and efficiency.

Foundation Support

河南省科技攻关计划项目(232102211057)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0403
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 6

Publish History

[2023-11-16] Accepted Paper

Cite This Article

李功丽, 马婧雯, 范云. 一种梯度隐藏的安全聚类与隐私保护联邦学习 [J]. 计算机应用研究, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0403. (Li Gongli, Ma Jingwen, Fan Yun. Gradient-hiding secure clustering and privacy-preserving federated learning [J]. Application Research of Computers, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0403. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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