Collaborative Training in Sensor Networks: A graphical model approach

TL;DR

This paper introduces a graphical model-based framework for collaborative training in sensor networks, enabling efficient distributed inference under communication constraints through message-passing and sampling algorithms.

Contribution

It presents a novel approach that combines graphical models with distributed sensor training, addressing communication limitations and demonstrating effectiveness with concrete examples.

Findings

Effective collaborative training achieved in sensor networks.

Message-passing and sampling algorithms handle different problem complexities.

Demonstrated success through concrete experimental examples.

Abstract

Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.