Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

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 …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Over the last decade, smart cities (SC) have been developed worldwide. Implementing big
data and the internet of things improves the monitoring and integration of different …

Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

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 …

Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-
cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly …

The arrival of the fifth-generation technology standard for broadband cellular networks (5G)
and beyond 5G networks (B5G) rises the speed and robustness ceiling of communicating …

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-
time data and deploying machine learning models. Nonetheless, an individual IoT device …

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine
learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML …