The increasing availability of data generated by Internet of Things (IoT) and Industrial IoT
(IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest …

Federated learning (FL) enables collaborative training of machine learning (ML) models
while preserving user data privacy. Existing FL approaches can potentially facilitate …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing
smart devices in manufacturing and industrial processes. Industries based on IoT generate …

In the era of deep learning, federated learning (FL) presents a promising approach that
allows multi-institutional data owners, or clients, to collaboratively train machine learning …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

In the era of deep learning, federated learning (FL) presents a promising approach that
allows multi-institutional data owners, or clients, to collaboratively train machine learning …

The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has
opened up for the development of novel types of systems and services. Federated Learning …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …