Dynamic Average Diffusion with randomized Coordinate Updates
Social and Information Networks
2019-07-31 v2 Multiagent Systems
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.
Cite
@article{arxiv.1810.08901,
title = {Dynamic Average Diffusion with randomized Coordinate Updates},
author = {Bicheng Ying and Kun Yuan and Ali H. Sayed},
journal= {arXiv preprint arXiv:1810.08901},
year = {2019}
}