Energy is an essential, but often forgotten aspect in large-scale federated systems. As most
of the research focuses on tackling computational and statistical heterogeneity from the …

Edge-assisted Internet of Agriculture Things (Edge-IoAT) connects massive smart devices
managed by edge nodes to collect crop data for distributed computing, such as federated …

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

We consider an energy harvesting (EH) transmitter communicating with a receiver through
an EH relay. The harvested energy is used for data transmission, including the circuit energy …

In this paper, we propose an energy-efficient federated learning (FL) framework for the
energy-constrained devices over cloud radio access network (Cloud-RAN), where each …

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 study, we first present a framework that jointly optimizes energy harvesting and
information decoding for Internet of Things (IoT) devices, which are capable of simultaneous …

This paper proposes an over-the-air aggregation framework for federated learning (FL) in
broadband wireless networks where not only edge devices but also a base station (BS) has …

Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …

Model update compression is a widely used technique to alleviate the communication cost
in federated learning (FL). However, there is evidence indicating that the compression …