Sparsity-aware distributed learning

Abstract

In this chapter, the problem of sparsity-aware distributed learning is studied. In particular, we consider the setup of an ad-hoc network, the nodes of which are tasked to estimate, in a collaborative way, a sparse parameter vector of interest. Both batch and online algorithms will be discussed. In the batch learning context, the distributed LASSO algorithm and a distributed greedy technique will be presented. Furthermore, an LMS-based sparsity promoting algorithm, revolving around the l1 norm, as well as a greedy distributed LMS will be discussed. Moreover, a set-theoretic sparsity promoting distributed technique will be examined. Finally, the performance of the presented algorithms will be validated in several scenarios.