The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

To meet the stringent service requirements of 6G applications such as immersive cloud
eXtended Reality (XR), holographic communication, and digital twin, there is no doubt that …

Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

With the rapid development of the Internet of Everything (IoE), the number of smart devices
connected to the Internet is increasing, resulting in large-scale data, which has caused …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …

As the general development trend of the automotive industry, connected and autonomous
vehicles (CAVs) can be used to increase transportation safety, promote mobility choices …

In this work, the federated learning mechanism is introduced into the deep learning of
medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …

With the rapid development of computing technology, wearable devices make it easy to get
access to people's health information. Smart healthcare achieves great success by training …

In-home health monitoring has attracted great attention for the ageing population worldwide.
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …