Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Recent years have disclosed a remarkable proliferation of compute-intensive applications in
smart cities. Such applications continuously generate enormous amounts of data which …

Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …

Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …

The increasing number of mobile users has given impetus to the demand for high data rate
proximity services. The fifth-generation (5G) wireless systems promise to improve the …

With the growing cognizance about environmental concern and global warming-related to
communication technologies, the researchers have been seeking some solutions to …

The fifth generation wireless networks must provide fast and reliable connectivity while
coping with the ongoing traffic growth. It is of paramount importance that the required …

Vehicular fog computing has emerged as a cost-efficient solution for task processing in
vehicular networks. However, how to realize effective server recruitment and reliable task …

In recent years, device-to-device (D2D) communication has attained significant attention in
the research community. The advantages of D2D communication can be fully realized in …

In the beyond 5G/6G era, aerial edge computing (AEC) is expected to be used as significant
components in Internet of Things. AEC brings flexible deployment with Line-of-Sight …