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 …

To exploit massive amounts of data at mobile edge networks, federated learning (FL) has
been proposed as an attractive substitute for centralized machine learning. To improve the …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …

Federated learning (FL) provides a privacy-preserving approach to realizing networked
intelligence. However, the performance of FL is often constrained by the limited …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile
users (MUs) aim to reach a consensus on a global model with the help of a parameter server …

This paper proposes an over-the-air aggregation framework for federated learning (FL) in
broadband wireless networks where not only edge devices but also a base station (BS) has …

Federated learning (FL) is a widely used distributed learning framework. However,
constrained wireless environment and intrinsically heterogeneous data across devices can …

Federated learning (FL) is a promising technology which trains a machine learning model
on edge devices in a distributed manner orchestrated by a parameter server (PS). To realize …

Addressing the communication bottleneck inherent in federated learning (FL), over-the-air
FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep …