This paper addresses the problem of distributed training of a machine learning model over
the nodes of a wireless communication network. Existing distributed training methods are …

Client and Internet of Things devices are increasingly equipped with the ability to sense,
process, and communicate data with high efficiency. This is resulting in a major shift in …

Motivated by the increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

The areas of machine learning and communication technology are converging. Today's
communications systems generate a huge amount of traffic data, which can help to …

To build intelligent model learning in conventional architecture, the local data are required to
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …

Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

The number of devices connected to the Internet has already surpassed 1 billion. With the
increasing proliferation of mobile devices, the amount of data collected and transmitted over …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

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 …