LIPIcs.DISC.2023.40.pdf
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Brief Announcement: Distributed Derandomization Revisited
Authors
Sameep Dahal 
,
Francesco d'Amore ,
Henrik Lievonen ,
Timothé Picavet ,
Jukka Suomela
-
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Volume:
37th International Symposium on Distributed Computing (DISC 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Distributed Computing (DISC) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-10-05
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Acknowledgements
We thank the participants in our reading group at Aalto University for helpful discussions.
Cite As Get BibTex
Sameep Dahal, Francesco d'Amore, Henrik Lievonen, Timothé Picavet, and Jukka Suomela. Brief Announcement: Distributed Derandomization Revisited. In 37th International Symposium on Distributed Computing (DISC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 281, pp. 40:1-40:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.DISC.2023.40
Abstract
One of the cornerstones of the distributed complexity theory is the derandomization result by Chang, Kopelowitz, and Pettie [FOCS 2016]: any randomized LOCAL algorithm that solves a locally checkable labeling problem (LCL) can be derandomized with at most exponential overhead. The original proof assumes that the number of random bits is bounded by some function of the input size. We give a new, simple proof that does not make any such assumptions - it holds even if the randomized algorithm uses infinitely many bits. While at it, we also broaden the scope of the result so that it is directly applicable far beyond LCL problems.
Subject Classification
ACM Subject Classification
- Theory of computation → Distributed computing models
- Theory of computation → Pseudorandomness and derandomization
Keywords
- Distributed algorithm
- Derandomization
- LOCAL model
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References
- Yi-Jun Chang, Tsvi Kopelowitz, and Seth Pettie. An exponential separation between randomized and deterministic complexity in the local model. SIAM Journal on Computing, 48(1):122-143, 2019. URL: https://doi.org/10.1137/17M1117537.
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