CONVERGENCE OF A DISTRIBUTED PARAMETER ESTIMATOR FOR SENSOR NETWORKS WITH LOCAL AVERAGING OF THE ESTIMATES
Distributed and Collaborative Signal Processing
Přednášející: Jérémie Jakubowicz, Autoři: Pascal Bianchi, Gersende Fort, Walid Hachem, Jérémie Jakubowicz, LTCI Telecom ParisTech / CNRS, France
The paper addresses the convergence of a decentralized Robbins-Monro algorithm for networks of agents. This algorithm combines local stochastic approximation steps for finding the root of an objective function, and a gossip step for consensus seeking between agents. We provide verifiable sufficient conditions on the stochastic approximation procedure and on the network so that the decentralized Robbins-Monro algorithm converges to a consensus. We also prove that the limit points of the algorithm correspond to the roots of the objective function. We apply our results to Maximum Likelihood estimation in sensor networks.
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