We study nonconvex distributed optimization in multiagent networks with time-varying
(nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed …

We consider the problem of distributed learning, where a network of agents collectively aim
to agree on a hypothesis that best explains a set of distributed observations of conditionally …

This paper aims to develop distributed learning algorithms for Random Vector Functional-
Link (RVFL) networks, where training data is distributed under a decentralized information …

The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive
estimation of signals defined over graphs. Assuming the graph signal to be band-limited …

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing
problem under linear and log-linear combination rules. We show that under both …

The current big data deluge requires innovative solutions for performing efficient inference
on large, heterogeneous amounts of information. Apart from the known challenges deriving …

In this paper, we consider learning dictionary models over a network of agents, where each
agent is only in charge of a portion of the dictionary elements. This formulation is relevant in …

In this work, we study the task of distributed optimization over a network of learners in which
each learner possesses a convex cost function, a set of affine equality constraints, and a set …

The aim of this paper is to propose distributed strategies for adaptive learning of signals
defined over graphs. Assuming the graph signal to be bandlimited, the method enables …

The aim of this chapter is to give an overview of the recent advances related to sampling and
recovery of signals defined over graphs. First, we illustrate the conditions for perfect recovery …