In this paper, we study asynchronous federated learning (FL) in a wireless distributed
learning network (WDLN). To allow each edge device to use its local data more efficiently …

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 …

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 …

Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to
be a major part of the sixth generation (6G) networks. The UAVs connected to the base …

Recently, federated learning (FL) has sparked widespread attention as a promising
decentralized machine learning approach which provides privacy and low delay. However …

In distributed optimization, parameter updates from the gradient computing node devices
have to be aggregated in every iteration on the orchestrating server. When these updates …

Federated learning is a privacy-preserving and distributed training method using
heterogeneous data sets stored at local devices. Federated learning over wireless networks …

Federated learning (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …