  Intelligent Transportation System (ITS) has become one of the essential
components in Industry 4.0. As one of the critical indicators of ITS,
efficiency has attracted wide attention from researchers. However, the next
generation of urban traffic carried by multiple transport service providers may
prohibit the raw data interaction among multiple regions for privacy reasons,
easily ignored in the existing research. This paper puts forward a federated
learning-based vehicle control framework to solve the above problem, including
interactors, trainers, and an aggregator. In addition, the density-aware model
aggregation method is utilized in this framework to improve vehicle control.
What is more, to promote the performance of the end-to-end learning algorithm
in the safety aspect, this paper proposes an imitation learning algorithm,
which can obtain collision avoidance capabilities from a set of collision
avoidance rules. Furthermore, a loss-aware experience selection strategy is
also explored, reducing the communication overhead between the interactors and
the trainers via extra computing. Finally, the experiment results demonstrate
that the proposed imitation learning algorithm obtains the ability to avoid
collisions and reduces discomfort by 55.71%. Besides, density-aware model
aggregation can further reduce discomfort by 41.37%, and the experience
selection scheme can reduce the communication overhead by 12.80% while ensuring
model convergence.
