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Technology of Blockchain
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2270-2276

Blockchain based Industrial Internet of Things privacy protection collaborative learning system

Lin Fengbin1
Wang Can1
Wu Qiuxin1
Li Han1
Qin Yu2
Gong Gangjun3
1. School of Applied Science, Beijing Information Science & Technology University, Beijing 100192, China
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
3. Beijing Engineering Research Center of Energy Electric Power Information Security, North China Electric Power University, Beijing 102206, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0572

Abstract

To make full use of heterogeneous nodes data from IIoT to train high-accuracy models while protecting data privacy, this paper proposed a privacy-preserving two-stage collaborative learning system based on blockchain. Firstly, it used a grouped federated learning framework to divide participating nodes into different groups based on their computing power. Each group trained a global model suitable for its computing power through federated learning. Secondly, it introduced split learning to enable nodes to collaborate with mobile edge computing servers to train a larger scale model, and used differential privacy technology to further protect data privacy. It stored trained models on the blockchain, and used the consensus algorithm of the blockchain to further prevent attacks from malicious nodes and protect the security of the model. Finally, to combine the advan-tages of multiple heterogeneous global models and further improve model accuracy, it used the feature extractor of each global model to extract features from user data, and used these features as training datasets to train a higher accuracy complex model. Experimental results show that the performance of system on Fashion-MNIST and CIFAR-10 datasets is better than the performance of traditional federated learning. It is suitable for obtaining high-accuracy models in IIoT scenarios.

Foundation Support

国家自然科学基金资助项目(61604014)
国家重点研发计划资助项目(2022YFB3105102)
未来区块链与隐私计算高精尖中心资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0572
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Technology of Blockchain
Pages: 2270-2276
Serial Number: 1001-3695(2024)08-004-2270-07

Publish History

[2024-01-30] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

林峰斌, 王灿, 吴秋新, 等. 基于区块链的工业物联网隐私保护协作学习系统 [J]. 计算机应用研究, 2024, 41 (8): 2270-2276. (Lin Fengbin, Wang Can, Wu Qiuxin, et al. Blockchain based Industrial Internet of Things privacy protection collaborative learning system [J]. Application Research of Computers, 2024, 41 (8): 2270-2276. )

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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