Dynamic Average Diffusion with randomized Coordinate Updates

TL;DR

This paper introduces a randomized coordinate-descent based online learning method for distributed agents to track the average of dynamic signals, ensuring convergence through careful coordination and validated by simulations.

Contribution

It presents a novel randomized coordinate update strategy for distributed average tracking with theoretical convergence analysis.

Findings

Convergence of the proposed method is established.

Simulations demonstrate effective tracking of time-varying signals.

Coordination among agents is crucial for unbiased convergence.

Abstract

This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observation vector, and different agents may select different entries of their observations before engaging in a consultation step. Careful coordination of the interactions among agents is necessary to avoid bias and ensure convergence. We provide a convergence analysis for the proposed methods, and illustrate the results by means of simulations.