Distributed Learning for Cooperative Inference
Abstract
We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations. Agents do not know the network topology or the observations of other agents. We explore a variational interpretation of the Bayesian posterior density, and its relation to the stochastic mirror descent algorithm, to propose a new distributed learning algorithm. We show that, under appropriate assumptions, the beliefs generated by the proposed algorithm concentrate around the true parameter exponentially fast. We provide explicit non-asymptotic bounds for the convergence rate. Moreover, we develop explicit and computationally efficient algorithms for observation models belonging to exponential families.
Cite
@article{arxiv.1704.02718,
title = {Distributed Learning for Cooperative Inference},
author = {Angelia Nedić and Alex Olshevsky and César A. Uribe},
journal= {arXiv preprint arXiv:1704.02718},
year = {2017}
}