Communication Constrained Learning with Uncertain Models

We consider the problem of distributed inference of a group of agents in a social network, where the agents construct, share, and update beliefs in a non-Bayesian framework to identify the underlying true state of the world. We build upon the concept of uncertain models that accurately represents each agents knowledge of the distribution of each hypothesis based on the amount of training data collected. Then, we propose an event-triggered communication protocol that only transmits a belief for a hypothesis if new information has been incorporated since the previous communication time. We show that the proposed solution allows the agents to achieve beliefs within the neighborhood of a full communication network, while significantly reducing the amount of transmissions.