## 4 Multiple Edge Computing Platforms Enabled Air-Ground Network Architecture for Vehicular Scenarios  
In this section, we propose a VN architecture with multiple EC layers to serve VUs with diverse service requests. We characterize different EC platform layers in detail and highlight their importance for serving VUs. TN-based infrastructures are playing an important role in creating a fully functional intelligent VN for futuristic Smart City environments. However, they alone are not capable of providing adequate services to dynamic VUs, which often request services with extremely low latency and high reliability. TN is vulnerable to ground-based security \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Characteristics** & **Centralized Learning** & **Distributed Learning** & **Federated Learning** \\ \hline Learning Entity & Centralized Server & Distributed & Centralized & On Device (VUs) \\ \hline Communication Cost/Latency & High & High & Limited \\ \hline Computation Cost/Latency & High & Limited & Limited \\ \hline VUs Sensitive Data Privacy & Less & Less & High \\ \hline Useful for & \begin{tabular}{l} Training Fairly Complex \\ ML models (i.e., DNN with \\ limited number of layers) \\ \end{tabular} & \
\begin{tabular}{l} Training Complex ML \\ Models \\ \end{tabular} &
\begin{tabular}{l} Training Models with \\ Limited Complexity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the ML Models over VNs. attacks due to its fixed positions. Furthermore, in the case of natural disasters such as tsunamis, earthquakes users often fail to connect to the services of TN. Low accessibility into remote areas mainly because of the unwillingness of mobile operators to provide services into low revenue parts needs to be considered while integrating TN into VNs. NTN such as Air networks has a considerable advantage over TN in terms of availability, reliability, scalability, and low deployment costs. With these advantages, they can play an important role in complimenting TN for providing better quality services to VUs. Both TN and NTN platforms can enable EC-based services through placements of edge computing servers along with their distributed infrastructures. Therefore, a Multilayered joint T-NTN constituted by different EC platforms over TN and NTN can be utilized for providing heterogeneous services requested by VUs with demanded quality. In Figure 5, we propose an air-ground network having multiple EC platform layers and jointly exploiting TN and NTN for serving VUs. Several VUs are randomly distributed in the considered service area and demand many latency-critical and data-intensive services. The proposed network architecture is constituted by several elements as shown in Figure 5: a set of VUs, RSU, BS elements, LAPs, and HAP. For avoiding redundancies, in the following, we omit the legend from the figures. A set of RSUs is deployed alongside roads having a limited capacity EC servers for providing computation services to the VU. Each RSU can serve a limited set of VUs in its coverage range. VUs are also supported by the BS elements equipped with EC services. BS elements can sever VUs over larger coverage areas compared with RSUs and can have powerful EC servers. However, the required roundtrip time for sending and receiving back the VUs task can be much higher. Thus, RSUs and BC servers jointly forms form a multi-layered TN-based EC service platform for supporting VUs. VUs are also complemented by a swarm of LAPs (i.e., UAVs) deployed on top of them. UAVs can enable limited EC services with a pre-installed EC server. UAVs can have limited coverage and flight time that often limits their service range. VUs are also covered by multiple decentralized HAPs having better coverage and computing capacity compared with UAVs. Thus, Jointly, UAVs and HAPs form a multilayered NTN based EC platform for supporting VUs with better quality services and intelligent applications.