The advances in today's IoT devices and machine learning methods have given rise to the
concept of Federated Learning. Through such a technique, a plethora of network devices …

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 emerging advances in personal devices and privacy concerns have given the rise to the
concept of Federated Learning. Federated Learning proves its effectiveness and privacy …

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can
improve the driving experience and service quality. However, the bandwidth, security and …

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 …

The accuracy of the ML model is essential for the further development of AI-enabled CAVs.
With the increasing complexity of on-board sensor systems, the large amount of raw data …

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving,
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …

In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and
the rising privacy and security concerns, are two crucial challenges in terms of computation …

Machine learning (ML) algorithms are essential components in autonomous driving. In most
existing connected and autonomous vehicles (CAVs), a large amount of driving data …

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 …