On Adversary Robust Consensus Protocols Through Joint-Agent Interactions

A generalized family of adversary robust consensus protocols is proposed and analyzed. These are distributed algorithms for multiagent systems seeking to agree on a common value of a shared variable, even in the presence of faulty or malicious agents, which are updating their local state according to the protocol rules. In particular, we adopt monotone joint-agent interactions, a very general mechanism for processing locally available information and allowing cross-comparisons between state-values of multiple agents simultaneously. The salient features of the proposed class of algorithms are abstracted as a Petri net and convergence criteria for the resulting time evolutions formulated by employing structural invariants of the net.