  While federated learning (FL) improves the generalization of end-to-end
autonomous driving by model aggregation, the conventional single-hop FL (SFL)
suffers from slow convergence rate due to long-range communications among
vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such
drawbacks via introduction of mid-point edge servers. However, the
orchestration between constrained communication resources and HFL performance
becomes an urgent problem. This paper proposes an optimization-based
Communication Resource Constrained Hierarchical Federated Learning (CRCHFL)
framework to minimize the generalization error of the autonomous driving model
using hybrid data and model aggregation. The effectiveness of the proposed
CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform.
Results show that the proposed CRCHFL both accelerates the convergence rate and
enhances the generalization of federated learning autonomous driving model.
Moreover, under the same communication resource budget, it outperforms the HFL
by 10.33% and the SFL by 12.44%.
