# Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting

## TL;DR

This paper introduces a quantized, event-triggered distributed averaging algorithm for multi-agent systems with directed communication, ensuring finite-time consensus on a quantized average while reducing communication and energy costs.

## Contribution

It proposes a novel quantized, event-driven consensus algorithm that guarantees finite-time convergence on directed graphs, improving efficiency over existing methods.

## Key findings

- Achieves finite-time consensus on quantized average
- Operates effectively on directed, strongly connected graphs
- Reduces communication and energy consumption

## Abstract

We study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and rely on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol, illustrate its operation with an example, and show that its execution, on any timeinvariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value that is equal to the quantized average. We conclude with comparisons against existing quantized average consensus algorithms that illustrate the performance and potential advantages of the proposed algorithm.