The research in the sixth generation of wireless networks needs to tackle new challenges in
order to meet the requirements of emerging applications in terms of high data rate, low …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

This paper describes a novel algorithmic framework to minimize a finite-sum of functions
available over a network of nodes. The proposed framework, that we call GT-VR, is …

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …

The present contribution deals with decentralized policy evaluation in multi-agent Markov
decision processes using temporal-difference (TD) methods with linear function …

In this paper, we consider the decentralized dynamic optimization problem defined over a
multi-agent network. Each agent possesses a time-varying local objective function, and all …

Bayesian Federated Learning (FL) offers a principled framework to account for the
uncertainty caused by limitations in the data available at the nodes implementing …

This paper presents a distributed event-triggered unadjusted Langevin algorithm (DETULA)
to address the Bayesian learning problem. We consider a set of networked learning agents …

The exponential growth of wireless data traffic, driven by bandwidth-intensive applications
like virtual and augmented reality (VR/AR), emphasizes the need to exploit the abundant …