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

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

Conventional machine learning techniques are conducted in a centralized manner.
Recently, the massive volume of generated wireless data, the privacy concerns and the …

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 …

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 …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

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 …

Federated learning (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …

New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …

The advances of both computation and communication technologies facilitate the
exploitation of massive data generated by mobile devices. It is attractive to leverage these …