  Federated learning (FL) is a machine learning technique that aims at training
an algorithm across decentralized entities holding their local data private.
Wireless mobile networks allow users to communicate with other fixed or mobile
users. The road traffic network represents an infrastructure-based
configuration of a wireless mobile network where the Connected and Automated
Vehicles (CAV) represent the communicating entities. Applying FL in a wireless
mobile network setting gives rise to a new threat in the mobile environment
that is very different from the traditional fixed networks. The threat is due
to the intrinsic characteristics of the wireless medium and is caused by the
characteristics of the vehicular networks such as high node-mobility and
rapidly changing topology. Most cyber defense techniques depend on highly
reliable and connected networks. This paper explores falsified information
attacks, which target the FL process that is ongoing at the RSU. We identified
a number of attack strategies conducted by the malicious CAVs to disrupt the
training of the global model in vehicular networks. We show that the attacks
were able to increase the convergence time and decrease the accuracy the model.
We demonstrate that our attacks bypass FL defense strategies in their primary
form and highlight the need for novel poisoning resilience defense mechanisms
in the wireless mobile setting of the future road networks.
