Federated learning (FL) represents a distributed machine learning approach that leverages
a centralized server to train models while keeping the data on edge devices isolated. FL has …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …

Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we propose a service …

Federated learning (FL) enables clients to collaboratively learn a shared task while keeping
data privacy, which can be adopted at the edge of wireless networks to improve edge …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn a model without sharing their raw data. Despite its practical …

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) refers to the learning paradigm that trains machine learning models
directly in the decentralized systems consisting of smart edge devices without transmitting …

In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …