# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Federated Learning for IoV
One UAV is selected as the central unit; the other UAVs are used as FL clients. A neural network model is used at each FL client (UAV). The selection of the central unit is not discussed. [PERSON] et al. [105] integrates FL technology with digital twin technology for air-ground networks where a drone works as the central server of FL, and ground terminals work as FL clients. Digital twin technology is used to capture network dynamics in the training of the model. Numerical results are used to show the significance of the proposal in terms of learning accuracy and energy efficiency. In Ref. [106], a FL approach is discussed for image processing applications in vehicular IoT. In addition, a model aggregation method is introduced to improve the accuracy of the global model and minimize the communication overhead for model exchanges. The model aggregation method takes into account the quality of local images, and the computational capability on each candidate for FL client. [PERSON] et al. [107] propose a FL-based image classification system for multi-UAV networks. In this system, each UAV updates its local model based on the data from on-board cameras. A ground fusion center aggregates the local machine learning models generated by multiple UAVs to form a global model. The authors use computer simulations to demonstrate that the FL approach is able to reduce the communication cost significantly for a given target test accuracy, compared with the centralized training approach. [PERSON] et al. [108] propose a FL-based energy demand prediction method for electric vehicle (EV) networks. In this method, charging stations are the FL clients, and the smart grid provider is the central server aggregating local models. A deep neural network is used as the machine learning model. In addition, taking advantage of the characteristic that charging stations in close geographical locations always have similar numbers of transactions, and similar transaction parameters, they also employ a clustering approach to generate CS clusters in order to reduce the impact of biased prediction results and improve the prediction accuracy. In Ref. [109], [PERSON] et al. introduce an aerial-ground integrated air-quality-sensing framework using FL based on UAV swarms. This framework includes aerial UAV swarms and ground-sensing systems, where the aerial-sensing part uses UAV cameras and the ground-sensing part uses static monitoring stations. In order to enable collaborative aerial sensing among UAVs from different agencies, UAVs work as FL clients to conduct local training, avoiding the transmission of camera data to other entities. A CNN model is used to learn the mapping between camera data and air-quality level. An UAV is used as the central server, which aggregates the learned model from multiple FL clients. [PERSON] et al. [110] discuss the use of FL in energy demand and driving range prediction for electric vehicles. They consider two different types of learning models, namely, linear regression and neural network. EVs perform training of machine learning models based on local data, and the backend infrastructure (cloud) aggregates the local models to generate the global model. Different driving behaviors are considered by grouping drivers into multiple groups with a clustering technology, where each group defines its own model. They also discuss the effect of different extensions of the federated averaging algorithm on the FL performance. [PERSON] et al. [111] propose a secure federated learning framework for UAV-assisted crowdsensing applications. The UAVs train local models and send the models to a mobile edge computing (MEC) server, which could be installed on a base station for aggregation. Upon reception of a sensing task request, the MEC server selects a set of UAVs to perform the task. The framework can enable better utilities for UAVs than existing baselines. [PERSON] et al. [112] propose a hierarchical FL framework for knowledge sharing in IoV based on blockchain technology.