Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things
(IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by …

The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically
increased the scope of cyber threats and data privacy issues. Recently, federated learning …

Multi-center heterogeneous data are a hot topic in federated learning. The data of clients
and centers do not follow a normal distribution, posing significant challenges to learning …

The metaverse, which is at the stage of innovation and exploration, faces the dilemma of
data collection and the problem of private data leakage in the process of development. This …

In the medical era, wearables often manage and find the specific data points to check
important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length …

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes
the further development of intelligent medical services possible, but privacy leakage and …

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-
generation communication networks with their extraordinary flexibility, affordability, and the …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …