## 8 Conclusions  
In this study, we have proposed a UAV-assisted FL framework in the context of the IoV to overcome the challenges of VUs' intermittent connectivity, low proactivity, and limited resources. An incentive stage and a training stage are involved in this framework, where a contract-based incentive mechanism and an energy-efficient resource allocation algorithm are designed separately. With the assistance of UAVs, VUs can benefit from enhanced communication efficiency and mobility, thus ensuring better training performance. The experimental results show that the proposed framework achieves effectiveness in terms of incentives and outperforms the baseline methods in terms of FL performance. Due to the energy consumption during FL and the limited computing resources, VUs with a higher data quantity and quality might be reluctant to participate in federated training because their standalone local training could yield better-performing models. To address this challenge of low proactivity, we have designed a contract-based incentive mechanism that categorizes VUs into different types, each signing distinct contracts. Due to the information asymmetry between task publishers and VUs, UAVs serve as intermediaries to assist in contract signing. These contracts specify the amount of data required for training for each vehicle type and the corresponding rewards. This contract mechanism enhances the proactive involvement of VUs in training and effectively manages data resources. The corresponding experiments verify the effectiveness of the contract mechanism. During the federated training process, it is necessary to consider both model performance and the total energy consumption induced by training to comprehensively manage training resources. Given the variation in data collected by VUs and their limited computing and communication resources, the complexity of resource management increases. Existing resource management studies mostly focus on single metrics of federated training and rarely optimize both performance and energy consumption simultaneously. The proposed contract-based efficient resource allocation algorithm in this paper addresses both performance and energy consumption in FL. It formulates the energy minimization problem based on contract results. As the contract mechanism manages data based on VU types and determines the training iterations, it enhances the performance of FL. Moreover, by introducing VUs' participation willingness in the energy optimization problem, the algorithm effectively manages computing and communication resources for training, thereby reducing the total energy consumption. The experiments have demonstrated that the proposed algorithm reduces energy consumption and improves performance in FL. In summary, this study addresses the challenges of VUs' intermittent connectivity, low proactivity, and limited resources in the context of FL. In potential future work, we would primarily focus on two aspects. Firstly, while we have already considered the role of UAVs as edge servers to assist FL, future attention could be directed towards evaluating the contribution of UAVs to system communication metrics. This includes assessing the extent to which UAVs contribute to reducing the risk of communication interruptions and minimizing data transmission latency, among other factors. Secondly, we have not yet considered issues of cooperation and competition among UAVs. Future research could explore the formation of alliances among UAVs to enhance the overall efficiency of the system. Funding acquisition, S.L., J.M., and H.T. ; Investigation, S.L., Y.L., and Z.H. ; Methodology, S.L., Y.L., and Z.H. ; Project administration, S.L. and H.T. ; Resources, S.L. ; Writing--original draft, Y.L. and Z.H. ; Writing--review and editing, S.L., B.Z., and H.T. All authors have read and agreed to the published version of the manuscript. This work is funded by the Royal Society of Edinburgh-National Natural Science Foundation of China Joint Project (under grants No. 61701034 (NSFC) and No. 63007 (RSE)).