  Learning the relationships between various entities from time-series data is
essential in many applications. Gaussian graphical models have been studied to
infer these relationships. However, existing algorithms process data in a batch
at a central location, limiting their applications in scenarios where data is
gathered by different agents. In this paper, we propose a distributed sparse
inverse covariance algorithm to learn the network structure (i.e., dependencies
among observed entities) in real-time from data collected by distributed
agents. Our approach is built on an online graphical alternating minimization
algorithm, augmented with a consensus term that allows agents to learn the
desired structure cooperatively. We allow the system designer to select the
number of communication rounds and optimization steps per data point. We
characterize the rate of convergence of our algorithm and provide simulations
on synthetic datasets.
