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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

SDP-FL: federated learning framework based on selective differential privacy for IIoT

Liu Xuan1
Liu Ya1,2
Wang Xinzhong1
Zhao Fengyu3
Liu Xianbei4
1. Computer Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2. Hong Kong Lion Rock Network Security Laboratory Hong Kong 999077, China
3. Information and Intelligent Engineering, Shanghai Publishing and Printing College, Shanghai 200093, China
4. School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu Anhui 233030, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0265

Abstract

With the rapid development of the Industrial Internet of Things (IIoT) , efficiently utilizing and protecting device data has become a critical issue. Federated Learning (FL) , which trains models locally and shares model parameters, has emerged as an effective solution for ensuring data privacy. However, existing FL methods still pose privacy leakage risks. To address this, this paper proposes a Selective Differential Privacy Federated Learning (SDP-FL) framework for IIoT. The framework integrates endpoint devices in smart factories as clients for federated learning. On the client side, local models are protected with minimal pruning and Gaussian noise before uploading. On the server side, a selection mechanism based on the loss function difference sets the update threshold for model parameters, aggregating only high-quality local models. Experimental results show that the SDP-FL framework achieves classification accuracies of 97.8% and 79.2% on MNIST and CIFAR-10 datasets, respectively, improving upon traditional FL methods by 1.6% and 0.6%. The method effectively avoids the interference of useless gradients and simultaneously enhances the model aggregation utility.

Foundation Support

国家自然科学基金资助项目(62002184)
安徽省高校自然科学重点项目(2024AH050011)
香港狮子山网络安全实验室研究课题(LRL24017)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0265
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-18] Accepted Paper

Cite This Article

刘暄, 刘亚, 王新中, 等. SDP-FL：选择性差分隐私的工业物联网联邦学习框架 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0265. (Liu Xuan, Liu Ya, Wang Xinzhong, et al. SDP-FL: federated learning framework based on selective differential privacy for IIoT [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0265. )

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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