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

Federated learning (FL) has recently emerged as a promising technology to enable artificial
intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel
and synchronize their local models using over-the-air computation. The integrity of AirFL is …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

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 …

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving
fast model aggregation by exploiting the waveform superposition property of multiple-access …

To facilitate the deployment of machine learning in resource and privacy-constrained
systems such as the Internet of Things, federated learning (FL) has been proposed as a …