Dynamic Average Diffusion with randomized Coordinate Updates

Bicheng Ying; Kun Yuan; Ali H. Sayed

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arXiv · 1810.08901

Dynamic Average Diffusion with randomized Coordinate Updates

Bicheng Ying·Kun Yuan·Ali H. Sayed

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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.

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Bicheng Ying, Kun Yuan, Ali H. Sayed. 2018-10-21. Dynamic Average Diffusion with randomized Coordinate Updates. https://arxiv.org/abs/1810.08901

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