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Min-Max Consensus Algorithm for Multi-agent Systems Subject to Privacy-Preserving Problem

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11307))

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Abstract

This paper proposes a privacy-preserving min-max consensus algorithm for discrete-time multi-agent systems, where all agents not only can reach a common state asymptotically, but also can preserve the privacy of their states at each iteration. Based on the proposed algorithm, the detailed consensus analysis is developed, including the impossibility of finite time convergence and the sufficient condition of consensus. Moreover, the privacy-preserving analysis is provided to guarantee the reliability of our privacy-preserving scheme. Finally, a numerical simulation is performed to demonstrate the correctness of our results.

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Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Aijuan Wang, Nankun Mu & Xiaofeng Liao

Authors
  1. Aijuan Wang
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  2. Nankun Mu
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  3. Xiaofeng Liao
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Corresponding author

Correspondence to Nankun Mu .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, A., Mu, N., Liao, X. (2018). Min-Max Consensus Algorithm for Multi-agent Systems Subject to Privacy-Preserving Problem. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-04239-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04238-7

  • Online ISBN: 978-3-030-04239-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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