## 2 IoV Distributed Intelligence
### Overview of Distributed Learning Methods
This approach is particularly relevant for IoV applications where model inference can occur locally on vehicles. **Multi-Agent Reinforcement Learning (MARL):** When environmental dynamics influence agents' decisions, they must learn about these dynamics and adjust their methods based on experience gained via agent-to-environment and agent-to-agent interactions. In this regard, Reinforcement Learning (RL) with exploration and exploitation abilities is critical. Exploring in RL allows agents to understand the dependencies of their decisions on the environment and other agents (policy) and on the consequences (value), which may then be used to improve long-term rewards. Even in single-agent instances, the data necessary to understand policy and value might be dispersed over several agents acting as helpers. FL, FD, and GADMM can improve learning policies and value over distributed helpers despite communication and privacy constraints. Within the MARL paradigm, the interactions of several agents in the same environment while making decisions based on local observations are investigated [36]. MARL is classified into centralized/decentralized and cooperative/competitive frameworks based on the presence of a central controller and the sorts of interactions. Centralized MARL frameworks assume a central controller that learns decision-making rules by gathering all agents' experiences, which include observed states, actions taken, and rewards received. Exchanging such information may use a significant amount of communication and memory resources, while jeopardizing data privacy. Decentralized MARL without a central controller does not have these disadvantages, but it does not ensure individual agents' convergence to equilibrium policies, even in cooperative MARL where all workers aim for the same objective. In IoV, MARL can be applied to edge caching [37], cooperative navigation [38], traffic optimization [39], and other scenarios in which vehicles interact with each other. **Transfer Learning (TL):** With the involved dynamicity, resource limitations, and latency constraints, performing full-scale model training is not always feasible in vehicular environments. To this end, TL can be very useful for performing model training. In the case of TL, learning agents can utilize past learning experiences through knowledge transfer (KT) to perform new learning tasks. TL approaches can increase the convergence rate, minimize reliance on labeled data, and improve the robustness of machine learning techniques in different vehicle settings [40]. There are various forms of TL based on KT strategies. For example, the experiences gathered in terms of learning data, e.g., data features and learning data scope, can be transferred for efficient learning of target tasks. On the other hand, knowledge depending on a trained model, for example, the structure and parameters of the model, can also be shared with a target task to improve training performance [41]. The advantages, challenges, conditions, and Key Performance Indicators (KPIs) of these fundamental structures are illustrated in Table 1 in order to distinguish them in different applications. The main KPIs, which are used later in the case study section, including delay, preserved privacy, mobility of handled vehicles, and gained processing capability, are ranked in the last column of the table.