In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …

Newton-type methods are popular in federated learning due to their fast convergence. Still,
they suffer from two main issues, namely: low communication efficiency and low privacy due …

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile
devices to collaboratively train artificial intelligence networks without the centralized …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

The decentralized stochastic gradient method emerges as a promising solution for solving
large-scale machine learning problems. This paper studies the decentralized Markov chain …

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

This paper tackles a challenging decentralized consensus optimization problem defined
over a network of interconnected devices. The devices work collaboratively to solve a …

In this paper, we propose to solve a regularized distributionally robust learning problem in
the decentralized setting, taking into account the data distribution shift. By adding a Kullback …

Federated learning (FL) is an emerging technique that enables privacy-preserving
distributed learning. Most related works focus on centralized FL, which leverages the …