  Cooperative Intelligent Transport System (C-ITS) is a promising network to
provide safety, efficiency, sustainability, and comfortable services for
automated vehicles and road infrastructures by taking advantages from
participants. However, the components of C-ITS usually generate large amounts
of data, which makes it difficult to explore data science. Currently, federated
learning has been proposed as an appealing approach to allow users to
cooperatively reap the benefits from trained participants. Therefore, in this
paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning
(SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation
from data sensing. We further formulate a joint edge node association and
resource allocation problem under the proposed SHFL framework to prevent
personalities of heterogeneous road vehicles and achieve
communication-efficiency. To deal with our proposed Mixed integer nonlinear
programming (MINLP) problem, we introduce a distributed Alternating Direction
Method of Multipliers (ADMM)-Block Coordinate Update (BCU) algorithm. With this
algorithm, a tradeoff between training accuracy and transmission latency has
been derived. Numerical results demonstrate the advantages of the proposed
algorithm in terms of training overhead and model performance.
