H Xing, O Simeone, S Bi - IEEE Journal on Selected Areas in …, 2021 - ieeexplore.ieee.org
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 …
R Xin, UA Khan, S Kar - IEEE Transactions on Signal …, 2020 - ieeexplore.ieee.org
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 …
H Xing, O Simeone, S Bi - 2020 IEEE 21st international …, 2020 - ieeexplore.ieee.org
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 …
K Yuan, W Xu, Q Ling - IEEE Transactions on Signal …, 2020 - ieeexplore.ieee.org
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 …