Engineering consensus in static networks with unknown disruptors

Abstract

Distributed control can increase system scalability, flexibility, and redundancy. Foundational to such scalability via decentralisation is consensus formation, by which decision-making and coordination are achieved. However, decentralised multi-agent systems are inherently vulnerable to disruption. To develop a resilient consensus approach, inspiration is taken from the study of social dynamics; specifically, the Deffuant Model which evaluates the impact of tolerance in social systems. A dynamic protocol is presented enabling efficient consensus to be reached with an unknown number of disruptors present within a multi-agent system. By inverting typical social tolerance, agents filter out extremist non-standard opinions that would drive them away from consensus. This approach allows distributed systems to deal with unknown disruptions, without knowledge of the network topology or the numbers and behaviours of the disruptors, a general requirement of other resilient consensus algorithms. A disruptor-agnostic algorithm is particularly suitable to real-world applications where information regarding disruptors or network properties is typically unknown. Faster, tighter, and more robust convergence can be achieved across a range of scenarios with the social dynamics inspired algorithm presented herein, when compared with Mean-Subsequence-Reduced-type methods.