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 …

Federated learning (FL) is an emerging collaborative machine learning (ML) framework that
enables training of predictive models in a distributed fashion where the communication …

Motivated by high resource costs of centralized machine learning schemes as well as data
privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on …

In this paper, we consider a continuous-type Bayesian Nash equilibrium (BNE) seeking
problem in subnetwork zero-sum games, which is a generalization of either deterministic …

We propose an improved convergence analysis technique that characterizes the distributed
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …

In this article, we consider the problem of decentralized optimization over time-varying
directed networks. The network nodes can access only their local objectives, and aim to …

Distributed stochastic non-convex optimization problems have recently received attention
due to the growing interest of signal processing, computer vision, and natural language …

A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm
(FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy …

This paper focuses on a multi-agent zeroth-order online optimization problem in a federated
learning setting for target tracking. The agents only sense their current distances to their …

In this paper, we study a communication-constrained multi-agent zeroth-order online
optimization problem within the federated learning (FL) setting with application to target …