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The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set toredefine conventional transportation networks with advanced intelligentservices and applications. These technologies, including edge computing,Machine Learning (ML), and network softwarization, pose stringent requirementsfor latency, energy efficiency, and user data security. Distributed Learning(DL), such as Federated Learning (FL), is essential to meet these demands bydistributing the learning process at the network edge. However, traditional FLapproaches often require substantial resources for satisfactory learningperformance. In contrast, Transfer Learning (TL) and Split Learning (SL) haveshown effectiveness in enhancing learning efficiency in resource-constrainedwireless scenarios like ITS. Non-terrestrial Networks (NTNs) have recentlyacquired a central place in the 6G vision, especially for boosting thecoverage, capacity, and resilience of traditional terrestrial facilities.Air-based NTN layers, such as High Altitude Platforms (HAPs), can have addedadvantages in terms of reduced transmission distances and flexible deploymentsand thus can be exploited to enable intelligent solutions for latency-criticalvehicular scenarios. With this motivation, in this work, we introduce theconcept of Federated Split Transfer Learning (FSTL) in joint air-groundnetworks for resource-constrained vehicular scenarios. Simulations carried outin vehicular scenarios validate the efficacy of FSTL on HAPs in NTN,demonstrating significant improvements in addressing the demands of ITSapplications.

Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision