Distributed Learning for Cooperative Inference

Angelia Nedić; Alex Olshevsky; César A. Uribe

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arXiv · 1704.02718

Distributed Learning for Cooperative Inference

Angelia Nedić·Alex Olshevsky·César A. Uribe

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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.

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BibTeX RIS

Angelia Nedić, Alex Olshevsky, César A. Uribe. 2017-04-10. Distributed Learning for Cooperative Inference. https://arxiv.org/abs/1704.02718

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