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 …

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 …

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) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

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 …

To efficiently exploit the massive amounts of raw data that are increasingly being generated
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …

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) is a promising training paradigm to achieve ubiquitous intelligence
for future 6G communication systems. However, it is challenging to apply FL in 6G-enabled …

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a
global model with highly distributed data located on mobile devices. Aiming to optimize the …

Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …