We consider decentralized stochastic optimization with the objective function (eg data
samples for machine learning tasks) being distributed over n machines that can only …

Communication data rate and energy constraints are important factors which have to be
considered when investigating distributed coordination of multi-agent networks. Although …

We consider distributed iterative algorithms for the averaging problem over time-varying
topologies. Our focus is on the convergence time of such algorithms when complete …

An average consensus protocol is proposed for continuous-time double-integrator multi-
agent systems with measurement noises under fixed topologies. The time-varying control …

This chapter presents distributed algorithms for cooperative optimization among multiple
agents connected through a network. The goal is to optimize a global-objective function …

This paper considers the average consensus problem on a network of digital links, and
proposes a set of algorithms based on pairwise “gossip” communications and updates. We …

Most of the current literature focused on centralized learning is centered around the
celebrated average-consensus paradigm and less attention is devoted to scenarios where …

This work presents a contribution to the solution of the average agreement problem on a
network with quantized links. Starting from the well‐known linear diffusion algorithm, we …

We consider a convex unconstrained optimization problem that arises in a network of agents
whose goal is to cooperatively optimize the sum of the individual agent objective functions …

In the average consensus a set of linear systems has to be driven to the same final state,
which corresponds to the average of their initial states. This mathematical problem can be …