## 3 Proposed FL Framework in UAV-Assisted IoV  
We consider an application scenario that consists of task publishers, a fleet of UAVs, and VUs, as illustrated in Figure 1.  
Task publishers are automobile enterprises that want to train intelligent road applications, such as automatic driving applications, road landmark recognition applications, traffic management applications, etc. They employ VUs to utilize local data to train intelligent applications. The set of task publishers is denoted as \(\mathcal{A}=\{a_{1},a_{2},...,a_{K}\}\) and \(K\) is the number of publishers.  
Due to the limited coverage of UAVs, a UAV often serves as the wireless relay for a specific area of the road, and it is carrying a specific task from the task publishers. The UAV incentivizes VUs to participate and organizes VUs to train models within its coverage area. It is responsible for aggregating the models of VUs and distributing new models. Assuming that there are \(M\) UAVs, their set is defined as \(\mathcal{S}=\{s_{1},s_{2},...,s_{M}\}\). The UAV \(s_{i}\) at position \((x_{s,i},y_{s,i})\) has a coverage radius \(R_{s,i}\).  
VUs use their collected data for local training, and only need to upload the trained model parameters to UAVs. Assume that \(N\) VUs are selected to participate in training in one area. The set of VUs is denoted as \(\mathcal{C}=\{c_{1},c_{2},...,c_{N}\}\), and the location of \(c_{j}\) as \((x_{c,i},y_{c,j})\).  
Our proposed UAV-assisted FL framework in the IoV involves an incentive stage and a training stage, as shown in Figure 2.  
During the incentive stage, the UAV is responsible for signing contracts with VUs participating in FL within the coverage area. The VUs are stimulated to select contracts that align with their respective types and determine the data quantity needed for local training. In the training stage, UAVs organize the VUs for FL in their areas. Each VU conducts local training using the needed data quantity and allocated computing resources. Subsequently, the VUs upload the local model parameters to the UAV. The UAV aggregates local models of all participating VUs and updates the global model. The global model is then distributed to the VUs again, and the training process is iterated until the global model converges. It should be noted that if a VU exceeds the coverage range of the UAV in the current area during the training process, the UAV can obtain the model parameters of the VU through mobile vehicle tracking or model feedback. Each UAV submits the final global model to the corresponding task publisher.  
Figure 1: Application scenario.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Description** & **Symbol** \\ \hline Path loss between \(c_{j}\) and UAV at timeslot \(t\) & \(g_{j}(t)\) \\ Transmission model size of \(c_{j}\) & \(\omega_{j}\) \\ Transmission power of VU \(c_{j}\) & \(p_{j}\) \\ CPU cycle frequency of \(c_{j}\) & \(f_{j}\) \\ Communication delay & \(T_{j}^{com}\) \\ Communication energy consumption & \(E_{j}^{com}\) \\ Computation delay & \(T_{j}^{train}\) \\ Computation energy consumption & \(E_{j}^{train}\) \\ Total delay of \(c_{j}\) & \(T_{j}\) \\ Total energy consumption of \(c_{j}\) & \(E_{j}\) \\ Local model accuracy of \(c_{j}\) & \(\eta_{i}^{loc}\) \\ Local training iterations of \(c_{j}\) & \(l_{j}^{loc}\) \\ Global training iterations of & \(l_{0}\) \\ Sojourn time of \(c_{j}\) under UAV \(s_{i}\) & \(T_{sojurn}^{ij}\) \\ The data quantity \(c_{j}\) contributed to task publisher & \(q_{j}\) \\ The reward of \(c_{j}\) & \(R_{j}\) \\ The willingness of \(c_{j}\) to participate in training & \(\varepsilon_{j}\) \\ The proportion of type-j VU & \(\rho_{j}\) \\ VU’s unit cost required for training & \(c\) \\ The utility of task publisher & \(U_{Tp}\) \\ The utility of \(c_{j}\) & \(U_{j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 2: UAV-assisted FL framework.