Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Abstract 5 G is the fifth generation of cellular networks. It enables billions of connected
devices to gather and share information in real time; a key facilitator in Industrial Internet of …

Wireless systems are vulnerable to various attacks such as jamming and eavesdropping
due to the shared and broadcast nature of wireless medium. To support both attack and …

In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …