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 …

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 …

Medical diagnostic image analysis (eg, CT scan or X-Ray) using machine learning is an
efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic …

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 …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient
paradigm of supporting resource-intensive applications on mobile devices. However, the …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the
dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article …