Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …

Federated Learning marks a turning point in the implementation of decentralized machine
learning (especially deep learning) for wireless devices by protecting users' privacy and …

The ongoing deployments of the Internet of Things (IoT)-based smart applications are
spurring the adoption of machine learning as a key technology enabler. To overcome the …

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 …

Federated learning (FL) rests on the notion of training a global model in a decentralized
manner. Under this setting, mobile devices perform computations on their local data before …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

The rapidly expanding number of Internet of Things (IoT) devices is generating huge
quantities of data, but public concern over data privacy means users are apprehensive to …

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 …

Motivated by the increasing computational capacity of wireless user equipments (UEs), eg,
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …

Federated learning has generated significant interest, with nearly all works focused on a
“star” topology where nodes/devices are each connected to a central server. We migrate …