  The recent application of Federated Learning algorithms in IOT and Wireless
vehicular networks have given rise to newer cyber threats in the mobile
environment which hitherto were not present in traditional fixed networks.
These threats arise due to the intrinsic nature of wireless transmission medium
and other inherent characteristics of mobile networks such as high-node
mobility and rapidly changing topology. This paper investigates the robustness
of Vehicular AttestedFL defense strategies against falsified information
attacks by tracking the behavior. We show that the defense strategies are
capable of detecting and eliminating malicious nodes in the wireless mobile
setting of the future smart road networks.
