Decentralized diffusion-based learning under non-parametric limited prior knowledge

The field of decentralized and distributed learning fits in with the area of modern Internet-of-Things (IoT) and wireless sensor networks (WSN) applications. Due to technological advances and functional advantages related to robustness and scalability [20], decentralized and distributed techniques are becoming more widespread in industry and are the subject of ongoing scientific research. Among various goals specific for learning and inference in decentralized networks, like learning linear modules [15], distributed economic dispatch [6] or target tracking [11], one can point out estimation tasks as in [2] or [4]. In this scenario, sensors (or agents) are scattered around a given area and collect data about an unknown phenomenon, modelled as a nonlinear function m: R R. Due to potential communication restrictions and the lack of dedicated fusion centers, agents may rely only on their local/private measurements and available network information. Following [12] and [7] we begin with brief summary of a few well-known strategies.