Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2473-2480

Federated learning method based on differential privacy protection knowledge transfer

Xu Chenyang1a,1b
Ge Lina1a,1b,2
Wang Zhe1a,1b,2
Zhou Yongquan1a,2
Qin Xia1a,1b
Tian Lei1b,1c
1. a. School of Artificial Intelligence, b. Key Laboratory of Network Communication Engineering, c. School of Electronic Information, Guangxi Minzu University, Nanning 530006, China
2. Guangxi Key Laboratory of Hybrid Computation & IC Design Analysis, Nanning 530006, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0813

Abstract

Federated learning solves the data silo problem of machine learning. However, the dataset of each party may have large differences in the instance space and feature space, which led to the degradation of prediction accuracy of the federated model. To address the above problems, this paper proposed a federated learning method based on differential privacy protection knowledge transfer. The method used boundary-expanding locality-sensitive hashing to calculate the similarity between instances of each party, and carried out weighted training of instances according to the similarity to achieve instance-based fede-rated transfer learning. In the above process, each party didn't need to disclose their instances to other parties, which could prevent the direct leakage of privacy. Meanwhile, to reduce the indirect privacy leakage in the knowledge transfer process, the proposed method introduced differential privacy mechanism to perturb the gradient data transmitted between all parties, so as to achieve privacy protection in the process of knowledge transfer. Theoretical analysis shows that the knowledge transfer process satisfies ε-differential privacy protection. This paper implemented the proposed method based on the XGBoost model. The experimental results show that, compared with the other methods without knowledge transfer, the proposed method reduces the test error of the federated model by more than 6% on average.

Foundation Support

国家自然科学基金资助项目(61862007)
广西自然科学基金资助项目(2020GXNSFBA297103)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0813
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Technology of Information Security
Pages: 2473-2480
Serial Number: 1001-3695(2023)08-037-2473-08

Publish History

[2023-03-13] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

徐晨阳, 葛丽娜, 王哲, 等. 基于差分隐私保护知识迁移的联邦学习方法 [J]. 计算机应用研究, 2023, 40 (8): 2473-2480. (Xu Chenyang, Ge Lina, Wang Zhe, et al. Federated learning method based on differential privacy protection knowledge transfer [J]. Application Research of Computers, 2023, 40 (8): 2473-2480. )

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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