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Asynchronous Non-Bayesian Learning in the Presence of Crash Failures

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Stabilization, Safety, and Security of Distributed Systems (SSS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10083))

Abstract

This paper addresses the problem of non-Bayesian learning in multi-agent networks, where agents repeatedly collect local observations about an unknown state of the world, and try to collaboratively detect the true state through information exchange. We focus on the impact of failures and asynchrony – two fundamental factors in distributed systems – on the performance of consensus-based non-Bayesian learning. In particular, we assume the networked agents may suffer crash faults, and messages delay can be arbitrarily long but finite.

  1. 1.

    We characterize the minimal global identifiability of the network for any consensus-based non-Bayesian learning to work.

  2. 2.

    Finite time convergence rate is obtained.

  3. 3.

    As part of our convergence analysis, we obtain a generalization of a celebrated result by Wolfowitz and Hajnal to submatrices, which might be of independent interest.

This research is supported in part by National Science Foundation award NSF 1329681. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies or the U.S. government.

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Notes

  1. 1.

    In this paper, every vector considered is column vector.

  2. 2.

    In the notation \(\mu _{t}^i\), the superscript denotes agents and subscript denotes iterations.

  3. 3.

    For a given graph, a node s is called a sink if it has no outgoing links.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, USA

    Lili Su & Nitin H. Vaidya

Authors
  1. Lili Su
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  2. Nitin H. Vaidya
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Corresponding author

Correspondence to Lili Su .

Editor information

Editors and Affiliations

  1. McMaster University, Hamilton, Ontario, Canada

    Borzoo Bonakdarpour

  2. LIP6, INRIA, UPMC Sorbonne Universities, Paris, France

    Franck Petit

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Su, L., Vaidya, N.H. (2016). Asynchronous Non-Bayesian Learning in the Presence of Crash Failures. In: Bonakdarpour, B., Petit, F. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2016. Lecture Notes in Computer Science(), vol 10083. Springer, Cham. https://doi.org/10.1007/978-3-319-49259-9_28

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  • DOI: https://doi.org/10.1007/978-3-319-49259-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49258-2

  • Online ISBN: 978-3-319-49259-9

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