Distributed Gaussian Learning over Time-varying Directed Graphs

The analysis of distributed (non-Bayesian) learning algorithm gained popularity since the seminal work of Jadbabaie et al. [1]. The ability of non-Bayesian updates to combine distributed optimization and learning algorithms make them especially useful for the design of distributed estimation algorithms with provable performance. In the distributed learning setup, a group of agents repeatedly receive signals about a certain unknown state of the world or parameter. No single agent has enough information to accurately estimate the unknown state and, thus, interaction with other agents is needed. Several results are readily available for performance evaluation of distributed learning algorithms for a variety of scenarios.