# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Federated Learning for IoV
Considering the diversity of driving routes and road environments, a hierarchical blockchain is introduced to improve the scalability of the proposed framework in terms of the network size. There are two types of FL clients and two types of blockchains, namely, vehicle client, RSU client, ground chain, and top chain, respectively. First, vehicles act as vehicle clients to train local models and then send the model updates to nearby RSUs. Then, RSUs collaborate with each other to record the model updates to the ground chain. RSU clients use the knowledge stored in the ground train and their own observations to train their models. RSU clients then record the learning results from both RSU clients and vehicle clients to the top chain, for the purpose of exchanging the information with other entities. [PERSON] et al. [113] propose a scheme for data sharing for IoV based on FL and blockchain technologies. A blockchain approach is employed to ensure the security. Based on the blockchain, an asynchronous FL is introduced to take into account the capability difference in FL clients. The central server selects the FL clients and the corresponding local training parameters in an adaptive approach by considering the capability of FL clients and the dynamic environments. [PERSON] et al. [114] propose a mobility-aware FL approach for content caching in IoV. Vehicles exchange content caching experience with each other using a FL-based approach, where vehicles are FL clients and RSUs are central servers. Each FL client trains a neural network based on local data related to content caching, and sends the learned experience to a RSU in proximity. A mobility-aware FL client selection approach is also proposed to ensure high-mobility training topology. The client selection considers the channel quality, connection time, and the importance of local data. [PERSON] et al. [115] propose a FL-based approach to enable distributed machine learning for UAV-Enabled IoV scenarios, including vehicle traffic prediction and parking slot management. A model owner allocates training tasks to UAVs. The whole geographical area is divided into multiple subregions, where each region is allocated to one UAV for training. A multi-dimensional contract-matching-based incentive mechanism is also designed to facilitate the participation of more candidates in the client selection. [PERSON] et al. [116] propose a FL-based jamming defense mechanism for flying ad hoc networks. A Q-learning model is used in distributed learning of the best defense policy. Possible movements of UAVs are defined as the actions. UAVs learn the best defense policy by interacting with environments, and the local models are aggregated at an edge computing server such as a base station. By sharing Q-values among multiple UAVs with FL, the convergence speed at each vehicle can be reduced. [PERSON] and [PERSON] [117] design a blockchain-based decentralized FL approach for IoV. Vehicles act as FL clients, and exchange information with other network entities through a blockchain. The model updates from multiple FL clients are also aggregated by the blockchain network. Without discussing the details of the FL scheme, Ref. [117] discusses the effect of blockchain parameters and wireless channel parameters on the system-level performance. There are also some studies discussing underlying technologies that better support FL in vehicular environments. [PERSON] et al. [118] consider a FL scenario for mobile devices with limited battery supplies. They propose using UAVs to transfer power to mobile devices to enable sustainable FL-based wireless systems. They focus on UAV resource allocation and UAV placement. [PERSON] et al. [119] consider some FL-enabled use cases for vehicular IoT, and discuss fog computing architecture for V2X networks.