###### Abstract  
With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs' intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs' contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%. federated learning; UAV-assisted Internet of Vehicles; incentive mechanism; resource allocation +
Footnote †: journal:  
0000-0002-4891-330X/00002-4891-330X/00002-4891-330X/00002-4891-330X/00002-4891-330X/00002-4891-330X/0002-4891-  
and coordination between vehicles. Inspired by the recent advancements in unmanned aerial vehicles' (UAVs') assisted cellular communications, we propose an UAV-assisted FL framework in the Internet of Vehicles (IoV) network, which can bring several advantages and motivations:  
1. Edge computing capabilities: UAVs can serve as edge services for federated learning, performing computation and learning tasks locally. This reduces the need for extensive data transfers and minimizes latency. Edge computing on UAVs enhances the scalability and efficiency of federated learning in distributed environments. 2. Communication efficiency and mobility enhancement: By processing model aggregation locally on UAVs, there is a significant reduction in the need to transmit large amounts of raw data to a central server. What is more, UAVs can cover a wide geographic area efficiently, which addresses the training challenges associated with the high dynamicity of vehicles in IoV. 3. Privacy-preserving surveillance: Federated learning allows for model training on decentralized data, addressing privacy concerns. UAVs can collect data or models locally without transmitting sensitive information to a central server. Privacy-preserving techniques, such as differential privacy, can be integrated into federated learning to further protect individual privacy. 4. Network resilience: UAVs can operate in areas with limited network infrastructure or during network failures. Federated learning's decentralized nature makes it resilient to intermittent connectivity, aligning well with the mobility and varying connectivity conditions in the IoV. The proposed framework involves two stages: the incentive stage and the training stage. In the incentive stage, a contract-based incentive mechanism is designed to encourage VUs to proactively contribute their data. In the training stage, an energy-efficient resource allocation algorithm is designed between the UAVs and the VUs to manage the process of federated training, which allocates VUs' computing and communication resources according to the result of the contract. The contributions of this article are as follows:  
* We propose a UAV-assisted FL framework in the IoV. Each UAV acts as a central server to provide the model aggregation or model parameter relay in the sky, which increases the reliability of FL under the uncertain and high-mobility conditions in the IoV network. * We design a contract-based incentive mechanism between UAVs and UVs. UAVs make contracts to assist VUs in responding to task publishers' service requests and participating in federated training. Moreover, the contract mechanism determines VUs' contributed local data based on their specific types in the presence of information asymmetry. VUs can receive corresponding revenues according to their data contributions.