Local Distributed Decision
Pierre Fraigniaud, Amos Korman, David Peleg

TL;DR
This paper develops a complexity theory for distributed decision problems in the LOCAL model, exploring classes like LD, BPLD, and NLD, and analyzing the impact of randomness and oracles on local decision-making.
Contribution
It introduces new classes of distributed decision problems, proves separations among them, and presents complete problems, advancing the theoretical understanding of locality in distributed computing.
Findings
Randomization often does not aid in deciding hereditary languages.
Existence of an NLD-complete problem established.
Access to node count oracle enables solving all decidable languages.
Abstract
A central theme in distributed network algorithms concerns understanding and coping with the issue of locality. Inspired by sequential complexity theory, we focus on a complexity theory for distributed decision problems. In the context of locality, solving a decision problem requires the processors to independently inspect their local neighborhoods and then collectively decide whether a given global input instance belongs to some specified language. This paper introduces several classes of distributed decision problems, proves separation among them and presents some complete problems. More specifically, we consider the standard LOCAL model of computation and define LD (for local decision) as the class of decision problems that can be solved in constant number of communication rounds. We first study the intriguing question of whether randomization helps in local distributed computing,…
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