Dynamic Average Diffusion with randomized Coordinate Updates
arXiv:1810.08901
The paper proposes and analyzes an online learning method that uses random coordinate-descent updates for agents to track the average of changing distributed signals.
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.