A recent algorithmic family for distributed optimization, DIGing's, have been shown to have
geometric convergence over time-varying undirected/directed graphs [1]. Nevertheless, an …

This paper considers solving a class of optimization problems which are modeled as the
sum of all agents' convex cost functions and each agent is only accessible to its individual …

This paper is devoted to investigate binary classification in a distributed and on-line setting.
In the Big Data era, datasets can be so large that it may be impossible to process them using …

This paper addresses the problem of collaborative training of nonlinear classifiers using big,
distributed training data. The supervised learning strategy considered in this paper …

A new challenge for learning algorithms in cyber-physical network systems is the distributed
solution of big-data classification problems, ie, problems in which both the number of …

Recent advances in networked control and distributed systems require the development of
scalable algorithms that consider the decentralized characteristic of the problem and …

IDENTIFICATION OF OVERCOMPLETE DICTIONARIES AND THEIR APPLICATION IN
DISTRIBUTED CLASSIFICATION PROBLEMS Page 1 IDENTIFICATION OF …