By deploying machine-learning algorithms at the network edge, edge learning can leverage
the enormous real-time data generated by billions of mobile devices to train AI models …

In distributed optimization, parameter updates from the gradient computing node devices
have to be aggregated in every iteration on the orchestrating server. When these updates …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

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 …

In this paper, we consider a problem in which distributively extracted features are used for
performing inference in wireless networks. We elaborate on our proposed architecture …

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 …

By deploying machine learning algorithms at the network edge, edge learning recently
emerges as a promising framework to support intelligent mobile services. It effectively …

Under the organization of the base station (BS), wireless federated learning (FL) enables
collaborative model training among multiple devices. However, the BS is merely responsible …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …