Ying, BichengYuan, KunSayed, Ali H.2019-11-122019-11-122019-11-122019-12-0110.1109/TSIPN.2019.2942191https://infoscience.epfl.ch/handle/20.500.14299/162858WOS:000492993200011This 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.Engineering, Electrical & ElectronicTelecommunicationsEngineeringTelecommunicationsheuristic algorithmsindexesconvergenceoptimizationinformation processingdistributed algorithmsnetwork topologydynamic average diffusionconsensuspush-sum algorithmcoordinate descentexact diffusiondistributed optimizationdescent methodconvergenceconsensusstrategiesalgorithmslimitstext::journal::journal article::research article