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.

本研究工作中，通过对分割学习和联邦学习优势的结合，提出了一种适应性分割联邦学习方案，用于车辆边缘计算。此方案通过自适应地分割模型并并行化训练过程，考虑了移动车辆选择和资源分配，极大地降低了训练延迟，并适应了网络动态和车辆的移动性。