On the convergence time of asynchronous distributed quantized averaging algorithms

TL;DR

This paper introduces a class of asynchronous distributed quantized averaging algorithms suitable for various network topologies, analyzing their convergence time with polynomial bounds using graph random walks.

Contribution

It proposes new quantized averaging algorithms for asynchronous networks with finite resources and provides polynomial bounds on their expected convergence time.

Findings

Algorithms work on fixed, switching, and random topologies.

Convergence time bounds are polynomial in network size.

Analysis uses random walk theory on graphs.

Abstract

We come up with a class of distributed quantized averaging algorithms on asynchronous communication networks with fixed, switching and random topologies. The implementation of these algorithms is subject to the realistic constraint that the communication rate, the memory capacities of agents and the computation precision are finite. The focus of this paper is on the study of the convergence time of the proposed quantized averaging algorithms. By appealing to random walks on graphs, we derive polynomial bounds on the expected convergence time of the algorithms presented.