We consider federated edge learning among mobile devices that harvest the required
energy from their surroundings, and share their updates with the parameter server (PS) …

Distributed learning on edge devices has attracted increased attention with the advent of
federated learning (FL). Notably, edge devices often have limited battery and …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

Federated learning (FL) has been widely regarded as a promising distributed machine
learning technology that utilizes on-device computation while protecting clients' data privacy …

Federated edge learning (FEEL) is a widely adopted framework for training an artificial
intelligence (AI) model distributively at edge devices to leverage their data while preserving …

In the Internet of Things, learning is one of most prominent tasks. In this paper, we consider
an Internet of Things scenario where federated learning is used with simultaneous …

A vital challenge in the deployment of federated learning (FL) over wireless networks is the
high energy consumption incurred for the local computation and model update upload on …

Federated learning (FL) is a promising technique for distilling artificial intelligence from
massive data distributed in Internet-of-Things networks, while keeping data privacy …

Federated learning (FL) has become a promising solution to train a shared model without
exchanging local training samples. However, in the traditional cloud-based FL framework …

In this letter, we study distributed federated learning (FL) in wireless powered
communication networks (WPCNs). The proposed system model ensures data privacy and …