Next-generation wireless networks are witnessing an increasing number of clustering
applications, and produce a large amount of non-linear and unlabeled data. In some …

In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …

Owing to their fast convergence, second-order Newton-type learning methods have recently
received attention in the federated learning (FL) setting. However, current solutions are …

This paper considers the problem of distributed learning (DL) in the presence of stragglers.
For this problem, DL methods based on gradient coding have been widely investigated …

In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …

Recently, mobile and embedded devices worldwide generate a massive amount of data at
the network edge. To efficiently exploit the data from distributed devices, we concentrate on …

This article proposes a communication-efficient decentralized deep learning algorithm,
coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk …

Federated reinforcement learning (FedRL) enables agents to collaboratively train a global
policy without sharing their individual data. However, high communication overhead …

In this paper, we propose (i) a novel distributed algorithm for consensus optimization over
networks and (ii) a robust extension tailored to deal with asynchronous agents and packet …

This paper considers the decentralized optimization problem, where agents in a network
cooperate to minimize the sum of their local objective functions by communication and local …