## 6 Simulation Results and Discussion
### Discussion and Comparison with Existing Works  
As shown in Table 5, compared with the previous machine learning approaches whose result is reported as 50% reduction in model convergence time, our method achieves around 30% reduction in latency for similar model convergence time. Furthermore, when the average UAV transmit power is between 1 and 10 dBm, the average task processing time of the gross data offloading and the FL scenarios varies from 5 to 22 ms, respectively. With the increase in packet size for a higher vehicle speed of \(V=80\) km/h and larger road segment length 4 km, the average UAV transmit power is between 10 and 28 dBm.  
In this case, the average task processing time of the gross data offloading and the FL scenarios varies from 20 to 35 ms, respectively. When the UAV altitude increases from 600 to 1000 m, the average UAV transmit power varies between 15 and 35 dBm. The flying time is constrained by the average task processing time and number of vehicles in a road segment, respectively. For FL scenario, when the UAV transmit power is above 20 dBm, the processing and queuing delay becomes negligible as compared to the gross data offloading, as part of computation is performed at the edge nodes in the FL scenario. When the average UAV transmit power is higher than 10 dBm, and when the SNR is low, the data offloaded by the vehicles are queued. The average task processing time is determined by its local computation time in the case of FL vs. gross data offloading.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Proposed Method** & **Objectives** & **Cost Function** & & **Reported Results** \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{*} & \multirow{3}{*}{An alternative mechanism to SPS} & \multirow{3}{*}{*} & \(\bullet\) & Lower packet–drop ratio & \(\bullet\) & Delay & \multirow{3}{*}{*} \\  & & & Priority-based strategy to retransmit dropped & & & Number of messages dropped & & \(\bullet\) \\ \hline \multirow{3}{*}{[72]} & \multirow{3}{*}{*} & \multirow{3}{*}{FL-based network optimization} & \multirow{3}{*}{*} & \(\bullet\) & Minimized FL model training losses and convergence time & \(\bullet\) & Node selection & \(\bullet\) & 56\% less model convergence time and 3\% accuracy enhancement \\ \hline \multirow{3}{*}{[73]} & \multirow{3}{*}{*} & \multirow{3}{*}{Power optimization} & \multirow{3}{*}{*} & Lesser queuing delay & \multirow{3}{*}{\(\bullet\)} & Delay & \multirow{3}{*}{\(\bullet\)} & \(\bullet\) \\  & & & Optimal power allocation & & & & & \\  & & Delay minimization & & & & & \\ \hline \multirow{3}{*}{[74]} & \multirow{3}{*}{*} & \multirow{3}{*}{Integration of edge–cloud platform} & \multirow{3}{*}{*} & Discrete action space Edge–cloud node differentiation & \(\bullet\) & Power consumption & \(\bullet\) & 56\% less power consumption \\  & & & & differentiation & & & & \\  & & & Workflow scheduling efficiency & & & & & \\ \hline \multirow{3}{*}{[75,76]} & \multirow{3}{*}{*} & \multirow{3}{*}{Dempster–Shafer theory-based reputation updates} & \multirow{3}{*}{*} & \(\bullet\) & Periodic feedback & \multirow{3}{*}{\(\bullet\)} & More vehicles incorporated in the architecture \\  & & & Optimal updates based on partial collaboration between participating nodes & & & & \\  & & & nodes & & & & \\ \hline \multirow{3}{*}{Our Work} & \multirow{3}{*}{*} & \multirow{3}{*}{Minimize the number of communication rounds between vehicles and UAV \(\bullet\)} & \multirow{3}{*}{Delay} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(10\%\) low-latency communication between vehicles (\(V\)) and UAV vehicles (\(V\)) and UAV and 15\% fast model convergence compared to [72, 73, 74]} \\  & & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different federated learning and deep learning methods for performance enhancement of UAV–vehicle communications.