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Computing in a Faulty Congested Clique

Authors Keren Censor-Hillel , Pedro Soto

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LIPIcs.OPODIS.2025.10.pdf
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ACM Subject Classification
  • Theory of computation → Distributed algorithms
Keywords
  • distributed computing
  • graph algorithms
  • computing with faults

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Abstract

We study a Faulty Congested Clique model, in which an adversary may fail nodes in the network throughout the computation. We show that any task of O(nlog{n})-bit input per node can be solved in roughly n rounds, where n is the size of the network. This nearly matches the linear upper bound on the complexity of the non-faulty Congested Clique model for such problems, by learning the entire input, and it holds in the faulty model even with a linear number of faults.
Our main contribution is that we establish that one can do much better by looking more closely at the computation. Given a deterministic algorithm 𝒜 for the non-faulty Congested Clique model, we show how to transform it into an algorithm 𝒜' for the faulty model, with an overhead that could be as small as some logarithmic-in-n factor, by considering refined complexity measures of 𝒜. 
As an exemplifying application of our approach, we show that the O(n^{1/3})-round complexity of semi-ring matrix multiplication [Censor{-}Hillel, Kaski, Korhonen, Lenzen, Paz, Suomela, PODC 2015] remains the same up to polylog factors in the faulty model, even if the adversary can fail 99% of the nodes (or any other constant fraction).

Cite As Get BibTex

Keren Censor-Hillel and Pedro Soto. Computing in a Faulty Congested Clique. In 29th International Conference on Principles of Distributed Systems (OPODIS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 361, pp. 10:1-10:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.OPODIS.2025.10

Author Details

Keren Censor-Hillel
  • Technion, Haifa, Israel
Pedro Soto
  • Virginia Tech, Blacksburg, VA, USA

Funding

  • Censor-Hillel, Keren: is supported in part by the Israel Science Foundation, grant 529/23.

Acknowledgements

We thank Orr Fischer, Ran Gelles, and Merav Parter for useful discussions.

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