  The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to
redefine conventional transportation networks with advanced intelligent
services and applications. These technologies, including edge computing,
Machine Learning (ML), and network softwarization, pose stringent requirements
for latency, energy efficiency, and user data security. Distributed Learning
(DL), such as Federated Learning (FL), is essential to meet these demands by
distributing the learning process at the network edge. However, traditional FL
approaches often require substantial resources for satisfactory learning
performance. In contrast, Transfer Learning (TL) and Split Learning (SL) have
shown effectiveness in enhancing learning efficiency in resource-constrained
wireless scenarios like ITS. Non-terrestrial Networks (NTNs) have recently
acquired a central place in the 6G vision, especially for boosting the
coverage, capacity, and resilience of traditional terrestrial facilities.
Air-based NTN layers, such as High Altitude Platforms (HAPs), can have added
advantages in terms of reduced transmission distances and flexible deployments
and thus can be exploited to enable intelligent solutions for latency-critical
vehicular scenarios. With this motivation, in this work, we introduce the
concept of Federated Split Transfer Learning (FSTL) in joint air-ground
networks for resource-constrained vehicular scenarios. Simulations carried out
in vehicular scenarios validate the efficacy of FSTL on HAPs in NTN,
demonstrating significant improvements in addressing the demands of ITS
applications.
