  Diverse critical data, such as location information and driving patterns, can
be collected by IoT devices in vehicular networks to improve driving
experiences and road safety. However, drivers are often reluctant to share
their data due to privacy concerns. The Federated Vehicular Network (FVN) is a
promising technology that tackles these concerns by transmitting model
parameters instead of raw data, thereby protecting the privacy of drivers.
Nevertheless, the performance of Federated Learning (FL) in a vehicular network
depends on the joining ratio, which is restricted by the limited available
wireless resources. To address these challenges, this paper proposes to apply
Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN.
Specifically, a vehicle selection and transmission power control algorithm is
developed to exploit the power domain differences in the received signal to
ensure the maximum number of vehicles capable of joining the FVN. Our
simulation results demonstrate that the proposed NOMA-based strategy increases
the joining ratio and significantly enhances the performance of the FVN.
