## 3 Network Sliced Distributed Learning
### DLaaS Architecture for IoV  
Different IoV parameters, such as VUs' geographical positions, speed, edge node densities, application requirements, etc., can impact the VUs' demands for one of the available DL slices. Here, in Figure 3, we consider a case study of realistic IoV scenarios with different slice demands. A centralized orchestrator/manager considers the scenario requirements where, thanks to specific DL slice descriptors, is able to deploy the Learning Functions to the different nodes. To this aim, proper descriptions of T/NTN layers are considered, where specific communications and computing capabilities are mapped. When assigning DL slices to the different VUs, they can be considered logically organized in clusters, where each cluster is characterized by specific applications, environmental conditions, and/or vehicular characteristics. As can be seen in Table 1, different DL algorithms can behave differently and, therefore, their selection depends on the selected IoV application, environmental conditions and characteristics of the IoV scenario, among others. For example, when using CFL, we must ensure that latency is not a critical factor, but we must also have a physical configuration that allows communication between nearby devices. To demonstrate the best approach, in Figure 3, we set up a configuration of vehicles, RSUs, LAPs, HAPs, and satellites with different physical conditions based on the three qualitative vehicle speed levels, vehicle/edge density and scenario dynamicity. Different application requirements, i.e., latency, privacy, and reliability, can dictate the choice of the DL slice along with the local environmental parameters. The orchestrator node must consider all these parameters and requirements before assigning the DL slices to the users. This approach can improve the performance of the DL in terms of model training costs, reliability, and user satisfaction. In Figure 3, Region 1 represents an area where a large group of VUs is moving slowly (e.g., city centers, ring roads, traffic hotspots in the city). In addition, the area is covered with a moderate number of edge nodes on the ground and in space, providing distributed computing/communication services. In such scenarios, VUs collaboration needs to be exploited for efficient DL processes. To this end, collaborative FL or SL can be the best choice. Region 2 highlights a road scenario with typical road environments (i.e., highways) having a limited number of edge nodes, to provide server VUs with steady speed. In such cases, the density of VUs will be limited, and collaborative learning might be challenging. Furthermore, with the limited number of edge nodes, advanced learning techniques, such as hierarchical FL, might not be feasible. However, with low edge node densities, only a few VUs might be able to connect to the servers. Such scenarios can be adequate to explain the benefits of GADMM-based DL. Region 3 lacks terrestrial/aerial edge nodes and stable connections between users due to challenging physical conditions (i.e., dense forests, and hillsides). Such cases can motivate the use of FL for privacy-preserving applications. Region 4 shows extremely good road conditions with high-speed VUs. Frequent handovers, limited training data, and unstable channel conditions can be some of the main concerns in these regions. In such cases, advanced learning methods, such as TL, can be ideal. Region 5 is similar to Region 2 with additional edge nodes. Such a high density of edge nodes can enable multilayer FL with hierarchical learning methods or MARL to improve latency and user data privacy. Additionally, collaborative learning between proximal edge nodes and VUs can also be available for strugglers, allowing more users to participate in DL training. Note that, although Figure 3 shows only one case of each IoV scenario, such conditions can be replicated throughout the service areas, and thus the orchestrator needs to consider  
Figure 3: Slice and DL Function Deployment for regions with different IoV characteristics.