In this article, we investigate a distributed online constrained optimization problem with
differential privacy where the network is modeled by an unbalanced digraph with a row …

This article presents communication-efficient approaches to federated learning (FL) for
resource-constrained devices with access to streaming data. In particular, we first propose a …

Most existing algorithms in decentralized online learning are conducted in the synchronous
setting. However, synchronization makes these algorithms suffer from the straggler problem …

Adaptive optimization methods, such as AdaGrad, RMSProp, and Adam, are widely used in
solving large-scale machine learning problems. A number of schemes have been proposed …

In this letter, we consider the binary classification problem via distributed Support Vector
Machines (SVMs), where the idea is to train a network of agents, with limited share of data …

In this article, we are interested in adaptive and distributed estimation of graph filters from
streaming data. We formulate this problem as a consensus estimation problem over graphs …

This paper considers the problem of decentralized consensus optimization over a network,
where each node holds a strongly convex and twice-differentiable local objective function …

This paper studies the decentralized optimization and learning problem where multiple
interconnected agents aim to learn an optimal decision function defined over a reproducing …

We investigate the generalisation performance of Distributed Gradient Descent with implicit
regularisation and random features in the homogenous setting where a network of agents …

Conditional mean embedding (CME) operators encode conditional probability densities
within Reproducing Kernel Hilbert Space (RKHS). In this paper, we present a decentralized …