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 …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

The fundamental communication paradigms in the next-generation mobile networks are
shifting from connected things to connected intelligence. The potential result is that current …

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …

We study federated edge learning (FEEL), where wireless edge devices, each with its own
dataset, learn a global model collaboratively with the help of a wireless access point acting …

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated
learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …