  The usage of federated learning (FL) in Vehicular Ad hoc Networks (VANET) has
garnered significant interest in research due to the advantages of reducing
transmission overhead and protecting user privacy by communicating local
dataset gradients instead of raw data. However, implementing FL in VANETs faces
challenges, including limited communication resources, high vehicle mobility,
and the statistical diversity of data distributions. In order to tackle these
issues, this paper introduces a novel framework for hierarchical federated
learning (HFL) over multi-hop clustering-based VANET. The proposed method
utilizes a weighted combination of the average relative speed and cosine
similarity of FL model parameters as a clustering metric to consider both data
diversity and high vehicle mobility. This metric ensures convergence with
minimum changes in cluster heads while tackling the complexities associated
with non-independent and identically distributed (non-IID) data scenarios.
Additionally, the framework includes a novel mechanism to manage seamless
transitions of cluster heads (CHs), followed by transferring the most recent FL
model parameter to the designated CH. Furthermore, the proposed approach
considers the option of merging CHs, aiming to reduce their count and,
consequently, mitigate associated overhead. Through extensive simulations, the
proposed hierarchical federated learning over clustered VANET has been
demonstrated to improve accuracy and convergence time significantly while
maintaining an acceptable level of packet overhead compared to previously
proposed clustering algorithms and non-clustered VANET.
