## 2 IoV Distributed Intelligence
### Overview of Distributed Learning Methods  
In this part, we will discuss the fundamental structures and the distinctions between centralized learning and various distributed learning approaches, which are illustrated in Table 1. For more explanation, see [3,24]. **Centralized Learning (CL):** In the case of Centralized Learning (CL), a set of distributed wireless nodes (e.g., vehicles) needs to communicate their collected local datasets over an unreliable communication channel to the centralized entities (i.e., base stations, clouds, etc.) for the ML model training. This process often results in higher data transmission costs, training latency, data security issues, etc. In the case of resource-constrained nodes, such as the IoV scenario, these issues become more critical. For this reason, DL is preferred to latency-critical VNs to perform various learning tasks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Learning Method** & **Advantages** & **Challenges** & **Conditions** & **KPIs** \\ \hline Centralized Learning (CL) & & & Sharing private data with a centralized controller & * & Devices’ modules to share their private data & \\  & & & Better performance for non-convex applications & Difficulty of implementation for data collection & * & Delay: High \\  & & & Less impact of communication link & * & * & * \\  & & imperfections on training & & * & * & * \\  & & & & * & * \\  & & & & * & * \\ \hline \multirow{4}{*}{**Federated Learning (FL)**} & & & Heterogeneous nodes with different amounts of data & * & * & \\  & & & Third-party attacks on parameter server (single input of failure) & * & * & \\  & & & & * & * \\  & & & & * & * \\  & & & & * & * \\  & & & & * & * \\  & & & & * \\  & & & & * & \\  & & & & * & \\  & & & & * & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages, challenges, conditions and KPIs for different learning paradigms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
In the following, the main DL methods are discussed in terms of characteristics, requirements and suitability to solve vehicular problems [25], where the main difference in DL versus CL is due to the fact that the ML model is trained at different locations and proper information exchange is performed among nodes. **Federated Learning (FL):** FL is one of the most widely used DL techniques, where wireless nodes perform distributed training operations with the help of a centralized parameter server. The FL process includes two main steps. First, devices with their local datasets perform the model training operation and communicate parameter updates to a server without the need to share their sensitive raw data. In the second step, the parameter server collects and aggregates the model updates from all devices to create a global learning model that has the benefit of aggregated training experiences from all devices. This global model is then used again by the nodes in the next FL iteration, allowing them to learn from the other devices' training experiences. It is especially helpful in IoV, where FL enables collaborative model training while respecting individual data ownership and security [26]. In some situations, the traditional centralized FL is not convenient and further optimization is needed. For this reason, in the recent past, different forms of FL have been proposed, especially to optimize FL performance according to learning environments [27]. Hierarchical FL with FL process distributed over several layers of edge devices [28], Federated Distillation (FD) [29], FL with transfer learning and FL with split learning [30] are examples of updated FL-based techniques. **Collaborative Federated Learning (CFL):** In reality, devices may not be able to connect to the central node due to energy constraints or possibly high transmission latency. To address this issue and make FL more accessible in real-world scenarios, the concept of CFL has been introduced, which allows vehicles to participate in FL without communicating with the central unit [31].