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 …

In this paper, we study distributed inference and learning over networks which can be
modeled by a directed graph. A subset of the nodes observes different features, which are …

Under the federated learning paradigm, a set of nodes can cooperatively train a machine
learning model with the help of a centralized server. Such a server is also tasked with …

This is Part II of a double-part special issue on distributed learning over wireless edge
networks. This two-part special issue features papers dealing with two main research …

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

With the advent of 5G and beyond (5GB) communications, decentralized Machine Learning
models in various 5GB use cases have become critical. However, wireless network settings …

In this article, we study device selection and resource allocation (DSRA) for layerwise
federated learning (FL) in wireless networks. For effective learning, DSRA should be …

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 …

Motivated by increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …