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Technology of Blockchain
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
356-360

Image classification model based on privacy-preserving federated learning and blockchain

Mao Qifan1
Wang Liangliang1,2
Wang Zihan1
1. College of Computer Science & Technology, Shanghai University of Electric Power, Shanghai 201306, China
2. Key Laboratory of Cryptography of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0246

Abstract

Conventional centralized image classification methods faced challenges due to data privacy issues and limitations in computing resources, rendering them inadequate for practical applications. Existing federated learning frameworks relied on central servers, and there were security challenges such as single points of failure and data poisoning attacks. To address these issues, this paper proposed a novel image classification scheme that combined privacy-preserving federated learning and blockchain technology. The scheme achieved reliability and security in image classification tasks within a distributed environment. This approach trained the image classification model through federated learning and uploaded to the blockchain network for verification and consensus. During the classification phase, the model obtained the final classification result through weighted combination. Experimental results demonstrate that the proposed scheme ensures the accuracy of image classification while protecting user privacy. In conclusion, this paper provides an effective approach to address data privacy and security concerns in image classification, and presents a positive exploration towards improving classification accuracy.

Foundation Support

国家自然科学基金资助项目(U1936213,61872230)
浙江省密码技术重点实验室开放研究基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0246
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Technology of Blockchain
Pages: 356-360
Serial Number: 1001-3695(2024)02-005-0356-05

Publish History

[2023-08-11] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

茆启凡, 王亮亮, 王子涵. 基于隐私保护联邦学习与区块链的图像分类方案 [J]. 计算机应用研究, 2024, 41 (2): 356-360. (Mao Qifan, Wang Liangliang, Wang Zihan. Image classification model based on privacy-preserving federated learning and blockchain [J]. Application Research of Computers, 2024, 41 (2): 356-360. )

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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