  In the pursuit of refining precise perception models for fully autonomous
driving, continual online model training becomes essential. Federated Learning
(FL) within vehicular networks offers an efficient mechanism for model training
while preserving raw sensory data integrity. Yet, FL struggles with
non-identically distributed data (e.g., quantity skew), leading to suboptimal
convergence rates during model training. In previous work, we introduced FedLA,
an innovative Label-Aware aggregation method addressing data heterogeneity in
FL for generic scenarios.
  In this paper, we introduce FedProx+LA, a novel FL method building upon the
state-of-the-art FedProx and FedLA to tackle data heterogeneity, which is
specifically tailored for vehicular networks. We evaluate the efficacy of
FedProx+LA in continuous online object detection model training. Through a
comparative analysis against conventional and state-of-the-art methods, our
findings reveal the superior convergence rate of FedProx+LA. Notably, if the
label distribution is very heterogeneous, our FedProx+LA approach shows
substantial improvements in detection performance compared to baseline methods,
also outperforming our previous FedLA approach. Moreover, both FedLA and
FedProx+LA increase convergence speed by 30% compared to baseline methods.
