  Autonomous driving is an active research topic in both academia and industry.
However, most of the existing solutions focus on improving the accuracy by
training learnable models with centralized large-scale data. Therefore, these
methods do not take into account the user's privacy. In this paper, we present
a new approach to learn autonomous driving policy while respecting privacy
concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to
train deep architectures in a fully decentralized manner and remove the need
for central orchestration. We design a new Federated Autonomous Driving network
(FADNet) that can improve the model stability, ensure convergence, and handle
imbalanced data distribution problems while is being trained with federated
learning methods. Intensively experimental results on three datasets show that
our approach with FADNet and DFL achieves superior accuracy compared with other
recent methods. Furthermore, our approach can maintain privacy by not
collecting user data to a central server.
