I. Overview
Recent advances in the field of distributed inference have produced several useful strategies aimed at exploiting local cooperation among network nodes to enhance the performance of each individual agent. However, the increasing availability of streaming data continuously flowing across the network has added the new and challenging requirement of online adaptation to track drifts in the data. In the adaptive mode of operation, the network agents must be able to enhance their learning abilities continually in order to produce reliable inference in the presence of drifting statistical conditions, drifting environmental conditions, and even changes in the network topology, among other possibilities. Therefore, concurrent adaptation (i.e., tracking) and learning (i.e., inference) are key components for the successful operation of distributed networks tasked to produce reliable inference under dynamically varying conditions and in response to streaming data.