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Non-IID federated learning via random exchange of local feature maps for textile IIoT secure computing

  • Research Paper
  • Special Focus on Cyber Security in the Era of Artificial Intelligence
  • Published:
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Abstract

With the fast development of artificial intelligence (AI) and industrial Internet of Things (IIoT) technologies, it is challenging to deal with the problems of data privacy protection and secure computing. In recent years, federated learning (FL) is proposed to attack the challenges of learning shared models collaboratively while protecting security based on the data from cross-domain clients. However, data in the real environment is usually not independent and identically distributed (Non-IID) due to the differences in business, working environments, and data acquisition, and thus classic federated methods suffer from significant performance degradation. In this paper, a novel federated framework is proposed for secure textile fiber identification (FedTFI) via cross-domain texture representation based on high-definition fabric images. In addition to sharing the gradient of FedTFI, the local patch of feature maps between cross-domain clients is randomly exchanged to build a richer image texture feature distribution while protecting data security simultaneously for secure computing. Furthermore, a texture embedding layer is designed to provide a joint representation through similarity measure between triplet samples in low-dimensional space. To verify the effectiveness of the proposed framework, two textile image datasets, i.e., one public and the other we collected, are utilized to construct four Non-IID scenarios, including label skew, feature skew, and two combined skew scenarios. The experimental results confirm the effectiveness of our model to obtain better detection accuracies than benchmarks in four Non-IID scenarios by keeping data privacy for secure computing in fabric IIoT.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 62171139) and Zhongshan Science and Technology Development Project (Grant No. 2020AG016).

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Authors and Affiliations

  1. School of Computer Science, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 200438, China

    Bo Peng, Mingmin Chi & Chao Liu

  2. Zhongshan PoolNet Technology Co. Ltd., Zhongshan Fudan Joint Innovation Center, Zhongshan, 528400, China

    Mingmin Chi

  3. Zhengzhou Zhongke Institute of Integrated Circuit and System Application, Zhengzhou, 450001, China

    Chao Liu

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  1. Bo Peng
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  2. Mingmin Chi
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  3. Chao Liu
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Correspondence to Mingmin Chi or Chao Liu.

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Peng, B., Chi, M. & Liu, C. Non-IID federated learning via random exchange of local feature maps for textile IIoT secure computing. Sci. China Inf. Sci. 65, 170302 (2022). https://doi.org/10.1007/s11432-021-3423-9

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  • DOI: https://doi.org/10.1007/s11432-021-3423-9

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