  Vehicular edge intelligence (VEI) is a promising paradigm for enabling future
intelligent transportation systems by accommodating artificial intelligence
(AI) at the vehicular edge computing (VEC) system. Federated learning (FL)
stands as one of the fundamental technologies facilitating collaborative model
training locally and aggregation, while safeguarding the privacy of vehicle
data in VEI. However, traditional FL faces challenges in adapting to vehicle
heterogeneity, training large models on resource-constrained vehicles, and
remaining susceptible to model weight privacy leakage. Meanwhile, split
learning (SL) is proposed as a promising collaborative learning framework which
can mitigate the risk of model wights leakage, and release the training
workload on vehicles. SL sequentially trains a model between a vehicle and an
edge cloud (EC) by dividing the entire model into a vehicle-side model and an
EC-side model at a given cut layer. In this work, we combine the advantages of
SL and FL to develop an Adaptive Split Federated Learning scheme for Vehicular
Edge Computing (ASFV). The ASFV scheme adaptively splits the model and
parallelizes the training process, taking into account mobile vehicle selection
and resource allocation. Our extensive simulations, conducted on
non-independent and identically distributed data, demonstrate that the proposed
ASFV solution significantly reduces training latency compared to existing
benchmarks, while adapting to network dynamics and vehicles' mobility.
