Ricerc@Sapienza

This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph. This formulation is relevant, for instance, in Big Data scenarios where massive amounts of data are collected/stored in different locations (e.g., sensors, clouds) and aggregating and/or processing all data in a fusion center might be inefficient or unfeasible, due to resource limitations, communication overheads or privacy issues.

Echo state networks (ESNs), belonging to the wider family of reservoir computing methods, are a powerful tool for the analysis of dynamic data. In an ESN, the input signal is fed to a fixed (possibly large) pool of interconnected neurons, whose state is then read by an adaptable layer to provide the output. This last layer is generally trained via a regularized linear least-squares procedure.

In this paper, we consider the problem of distributed unsupervised clustering, where training data is partitioned over a set of agents, whose interaction happens over a sparse, but connected, communication network. To solve this problem, we recast the well known Expectation Maximization method in a distributed setting, exploiting a recently proposed algorithmic framework for in-network non-convex optimization.