This study explores the benefits of integrating the novel clustered federated learning (CFL)
approach with non-orthogonal multiple access (NOMA) under non-independent and …

Driver distraction detection (3D) is essential in improving the efficiency and safety of
transportation systems. Considering the requirements for user privacy and the phenomenon …

The integration of federated learning and zero-trust security offers a promising solution for
enhancing wireless communication security. This comprehensive exploration examines the …

This paper investigates the robustness of over-the-air federated learning to Byzantine
attacks. The simple averaging of the model updates via over-the-air computation makes the …

With the rapid growth of connectivity and demands on reliable communications, channel
estimation becomes a critical problem. Recently, the advancement of deep learning enables …

Due to the dynamic nature of service requests and the uneven distribution of services in the
Internet of Vehicles (IoV), Multi-access Edge Computing (MEC) networks with pre-installed …

The rapid development of information communication and artificial intelligence (AI)
technology is driving innovation in various new application fields such as autonomous …

Cell-free massive MIMO is emerging as a promising technology for future wireless
communication systems, which is expected to offer uniform coverage and high spectral …

This paper presents a deep semantic communication system based on federated learning
with dynamic model aggregation, namely DeepSC-FedDMA, considering time-varying …

In this paper, we propose a Machine Learning (ML) based approach to address Wireless
Channel Estimation (WCE) problem for 5G and beyond wireless networks. Accurate wireless …