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 …

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 …

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

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 …

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 …

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 involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …

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 …

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 …