Randomized distributed decision

Pierre Fraigniaud, Mika Göös, Amos Korman, Merav Parter, David Peleg

Research output: Contribution to journalArticlepeer-review


The paper tackles the power of randomization in the context of local distributed computing by analyzing the ability to “boost” the success probability of deciding a distributed language using a Monte-Carlo algorithm. We prove that, in many cases, the ability to increase the success probability for deciding distributed languages is rather limited. This contrasts with the sequential computing setting where boosting can systematically be achieved by repeating the randomized execution.

Original languageEnglish
Pages (from-to)419-434
Number of pages16
JournalDistributed Computing
Issue number6
StatePublished - 23 Nov 2014
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.


  • Complexity classes
  • Distributed local algorithms
  • Randomized algorithms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics


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