Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated Learning (FL) is a promising paradigm for massive data mining service while
protecting users' privacy. In wireless federated learning networks (WFLNs), limited …

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely
considered for application in wireless networks for distributed model training. However, data …

As the proliferation of sophisticated task models in 5G-empowered digital twin, it yields
significant demands on fast and accurate model training over resource-limited wireless …

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 …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Existing device scheduling works in wireless federated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and require all devices to …

Machine learning (ML) is a widely accepted means for supporting customized services for
mobile devices and applications. Federated Learning (FL), which is a promising approach to …

Federated learning (FL) has emerged as a popular technique for distributing machine
learning across wireless edge devices. We examine FL under two salient properties of …