Existing device authentication techniques may suffer from heavy communication,
computation, and storage overhead for identifying a growing number of devices in …

The 5G and beyond-5G (B5G) is expected to be a key enabler for Internet-of-Everything
(IoE). The narrowband Internet of Things (NB-IoT) is a low-power wide-area enabling …

The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

A recent emphasis of distributed learning research has been on federated learning (FL), in
which model training is conducted by the data-collecting devices. Existing research on FL …

In federated learning (FL), reducing the communication overhead is one of the most critical
challenges since the parameter server and the mobile devices share the training parameters …

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground
integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles …

As Information, Communications, and Data Technology (ICDT) are deeply integrated, the
research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial …

Federated learning (FL) is capable of performing large distributed machine learning tasks
across multiple edge users by periodically aggregating trained local parameters. To address …

The demand for broadband wireless access is driving research and standardization of 5G
and beyond-5G wireless systems. In this article, we aim to identify emerging security …

Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL
has traditionally been studied for supervised ML tasks, in many applications, it is impractical …