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 …

Existing device scheduling methods in wireless fed-erated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and requires all devices …

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 …

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

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

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 …

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 …

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 …

This paper studies federated learning (FL) in a classic wireless network, where learning
clients share a common wireless link to a coordinating server to perform federated model …