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Noisy Rumor Spreading and Plurality Consensus

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Abstract

Error-correcting codes are efficient methods for handling noisy communication channels in the context of technological networks. However, such elaborate methods differ a lot from the unsophisticated way biological entities are supposed to communicate. Yet, it has been recently shown by Feinerman, Haeupler, and Korman [PODC 2014] that complex coordi- nation tasks such as rumor spreading and majority consensus can plausibly be achieved in biological systems subject to noisy communication channels, where every message trans- ferred through a channel remains intact with small probability 1 + ε, without using coding 2 techniques. This result is a considerable step towards a better understanding of the way biological entities may cooperate. It has nevertheless been established only in the case of 2-valued opinions: rumor spreading aims at broadcasting a single-bit opinion to all nodes, and majority consensus aims at leading all nodes to adopt the single-bit opinion that was initially present in the system with (relative) majority. In this paper, we extend this previous work to k-valued opinions, for any constant k ≥ 2. Our extension requires to address a series of important issues, some conceptual, others technical. We had to entirely revisit the notion of noise, for handling channels carrying k-valued messages. In fact, we precisely characterize the type of noise patterns for which plurality consensus is solvable. Also, a key result employed in the bivalued case by Feinerman et al. is an estimate of the probability of observing the most frequent opinion from observing the mode of a small sample. We generalize this result to the multivalued case by providing a new analytical proof for the bivalued case that is amenable to be extended, by induction, and that is of independent interest.

Dates and versions

hal-01423686 , version 1 (30-12-2016)

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Pierre Fraigniaud, Emanuele Natale. Noisy Rumor Spreading and Plurality Consensus. ACM Symposium on Principles of Distributed Computing (PODC), 2016, Chicago, United States. ⟨10.1145/2933057.2933089⟩. ⟨hal-01423686⟩
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