Resilient Randomized Quantized Consensus

TL;DR

This paper develops resilient consensus algorithms for multi-agent systems with quantized states, addressing malicious agents and asynchronous updates, using randomized methods to ensure robustness under network delays and adversarial conditions.

Contribution

It introduces a novel resilient quantized consensus scheme employing randomized MSR algorithms, with necessary and sufficient conditions based on graph robustness.

Findings

Resilient consensus achieved despite malicious agents.

Randomization in quantization and updates is essential for robustness.

Conditions for network robustness are characterized for consensus success.

Abstract

We consider the problem of multi-agent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integer-valued (i.e., quantized) states under directed communication links. The goal of the healthy normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. We develop update schemes to be equipped by the normal agents whose interactions are asynchronous and subject to non-uniform and time-varying time delays. In particular, we employ a variant of the so-called mean subsequence reduced (MSR) algorithms, which have been long studied in computer science, where each normal agent ignores extreme values from its neighbors. We solve the resilient quantized consensus problems in the presence of totally/locally bounded adversarial agents and provide necessary and sufficient conditions in terms of the connectivity notion of graph robustness. Furthermore, it will be shown that randomization is essential both in quantization and in the updating times when normal agents interact in an asynchronous manner. The results are examined through a numerical example.