  We introduce a distributed algorithm, termed noise-robust distributed maximum
consensus (RD-MC), for estimating the maximum value within a multi-agent
network in the presence of noisy communication links. Our approach entails
redefining the maximum consensus problem as a distributed optimization problem,
allowing a solution using the alternating direction method of multipliers.
Unlike existing algorithms that rely on multiple sets of noise-corrupted
estimates, RD-MC employs a single set, enhancing both robustness and
efficiency. To further mitigate the effects of link noise and improve
robustness, we apply moving averaging to the local estimates. Through extensive
simulations, we demonstrate that RD-MC is significantly more robust to
communication link noise compared to existing maximum-consensus algorithms.
