Systematic Research on Privacy-Preserving Distributed Machine Learning 


Vol. 13,  No. 2, pp. 76-90, Feb.  2024
https://doi.org/10.3745/TKIPS.2024.13.2.76


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  Abstract

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

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[IEEE Style]

M. S. Lee, Y. A. Shin, J. Y. Chun, "Systematic Research on Privacy-Preserving Distributed Machine Learning," The Transactions of the Korea Information Processing Society, vol. 13, no. 2, pp. 76-90, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.2.76.

[ACM Style]

Min Seob Lee, Young Ah Shin, and Ji Young Chun. 2024. Systematic Research on Privacy-Preserving Distributed Machine Learning. The Transactions of the Korea Information Processing Society, 13, 2, (2024), 76-90. DOI: https://doi.org/10.3745/TKIPS.2024.13.2.76.

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