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 …

As the 5G communication networks are being widely deployed worldwide, both industry and
academia have started to move beyond 5G and explore 6G communications. It is generally …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

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 …

The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …

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 …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

To facilitate the deployment of machine learning in resource and privacy-constrained
systems such as the Internet of Things, federated learning (FL) has been proposed as a …

The sixth-generation network (6G) is expected to achieve a fully connected world, which
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …