  This paper proposes a vehicular edge federated learning (VEFL) solution,
where an edge server leverages highly mobile connected vehicles' (CVs') onboard
central processing units (CPUs) and local datasets to train a global model.
Convergence analysis reveals that the VEFL training loss depends on the
successful receptions of the CVs' trained models over the intermittent
vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the
full device participation case (FDPC), the edge server aggregates client model
parameters based on a weighted combination according to the CVs' dataset sizes
and sojourn periods, while it selects a subset of CVs in the partial device
participation case (PDPC). We then devise joint VEFL and radio access
technology (RAT) parameters optimization problems under delay, energy and cost
constraints to maximize the probability of successful reception of the locally
trained models. Considering that the optimization problem is NP-hard, we
decompose it into a VEFL parameter optimization sub-problem, given the
estimated worst-case sojourn period, delay and energy expense, and an online
RAT parameter optimization sub-problem. Finally, extensive simulations are
conducted to validate the effectiveness of the proposed solutions with a
practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility
model.
