Federated learning (FL) provides a new paradigm for protecting data privacy in Industrial
Internet of Things (IIoT). To reduce network burden and latency brought by FL with a …

Federated learning (FL) has been increasingly considered to preserve data training privacy
from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT) …

Federated learning (FL) has been employed for numerous privacy-sensitive applications,
where distributed devices collaboratively train a global model. In industrial Internet of things …

Federated learning (FL) is a promising paradigm to empower on-device intelligence in
Industrial Internet of Things (IIoT) due to its capability of training machine learning models …

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent
manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to …

Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and
machine learning schemes for industries can assist in streamlining manufacturing …

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely
extended the border and capacity of artificial intelligence of things (AIoT) by providing a key …

The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the
digitalization of industrial production to achieve Industry 4.0. In this article, we propose a …

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the
large volumes of data they produce and growing concerns of data privacy. However, there …

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet
of Things (IoT) networks, thanks to preserving the privacy of users' data and efficient usage …