Asynchronous Non-Bayesian Learning in the Presence of Crash Failures

Conference paper

DOI: 10.1007/978-3-319-49259-9_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10083)
Cite this paper as:
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


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.



Distributed learning Crash failures Asynchrony 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations