With growth in the number of smart devices and advancements in their hardware, in recent
years, data-driven machine learning techniques have drawn significant attention. However …

Motivated by the increasing computational capacity of wireless user equipments (UEs), eg,
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …

Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

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 (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …