Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where
multiple devices collaboratively train a shared model without sharing their raw data with a …

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to
realize federated learning (FL). However, sensitive information of user equipments (UEs) …

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in
which wireless client-devices independently train their ML models and send the locally …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …

Reconfigurable intelligent surfaces (RISs) offer the potential to customize the radio
propagation environment for wireless networks. To fully exploit the advantages of RISs in …

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted over-the-air
federated learning (FL), where multiple antennas are deployed at each edge device to …

To support the unprecedented growth of the Internet-of-Things (IoT) applications and the
radio access of tremendous IoT devices, two new technologies have emerged recently to …

Uncertainty quantification plays a key role in the development of autonomous systems,
decision-making, and tracking over wireless sensor networks (WSNs). However, there is a …