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Conference Papers Year : 2020

Consensus vs Broadcast, with and without Noise


Consensus and Broadcast are two fundamental problems in distributed computing, whose solutions have several applications. Intuitively, Consensus should be no harder than Broadcast , and this can be rigorously established in several models. Can Consensus be easier than Broadcast? In models that allow noiseless communication, we prove a reduction of (a suitable variant of) Broadcast to binary Consensus, that preserves the communication model and all complexity parameters such as randomness, number of rounds, communication per round, etc., while there is a loss in the success probability of the protocol. Using this reduction, we get, among other applications, the first logarithmic lower bound on the number of rounds needed to achieve Consensus in the uniform GOSSIP model on the complete graph. The lower bound is tight and, in this model, Consensus and Broadcast are equivalent. We then turn to distributed models with noisy communication channels that have been studied in the context of some bio-inspired systems. In such models, only one noisy bit is exchanged when a communication channel is established between two nodes, and so one cannot easily simulate a noiseless protocol by using error-correcting codes. An Ω(ε −2 n) lower bound on the number of rounds needed for Broadcast is proved by Boczkowski et al. [PLOS Comp. Bio. 2018] in one such model (noisy uniform PULL, where ε is a parameter that measures the amount of noise). We prove an O(ε −2 log n) upper bound for binary Consensus in such model, thus establishing an exponential gap between the number of rounds necessary for Consensus versus Broadcast. We also prove a new O(ε −2 n log n) upper bound for Broadcast in this model.
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Dates and versions

hal-01958994 , version 1 (18-12-2018)
hal-01958994 , version 2 (29-11-2019)



Andrea Clementi, Luciano Gualà, Emanuele Natale, Francesco Pasquale, Giacomo Scornavacca, et al.. Consensus vs Broadcast, with and without Noise. ITCS 2020 - 11th Annual Innovations in Theoretical Computer Science, Jan 2020, Seattle, United States. pp.42 - 43, ⟨10.4230/LIPIcs.ITCS.2020.42⟩. ⟨hal-01958994v2⟩
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