  Street Scene Semantic Understanding (denoted as TriSU) is a crucial but
complex task for world-wide distributed autonomous driving (AD) vehicles (e.g.,
Tesla). Its inference model faces poor generalization issue due to inter-city
domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution
for improving TriSU model generalization, but suffers from slow convergence
rate because of vehicles' surrounding heterogeneity across cities. Going beyond
existing HFL works that have deficient capabilities in complex tasks, we
propose a rapid-converged heterogeneous HFL framework (FedRC) to address the
inter-city data heterogeneity and accelerate HFL model convergence rate. In our
proposed FedRC framework, both single RGB image and RGB dataset are modelled as
Gaussian distributions in HFL aggregation weight design. This approach not only
differentiates each RGB sample instead of typically equalizing them, but also
considers both data volume and statistical properties rather than simply taking
data quantity into consideration. Extensive experiments on the TriSU task using
across-city datasets demonstrate that FedRC converges faster than the
state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU,
mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative
evaluations in the CARLA simulation environment confirm that the proposed FedRC
framework delivers top-tier performance.
