## 5 Simulation Results and Discussion
### Variation in UAV's Transmit Power (dBm) with Number of Vehicles (\(V\))  
The variation in the UAV's transmit power (dBm) vs. number of vehicles (\(V\)) for FedAvg and PFL is illustrated in Figure 12. The variation in the UAV's transmit power (dBm) vs. number of vehicles (\(V\)) for FedSGD and PFL with attention mechanism is illustrated in Figures 13 and 14 for \(V\) = 1-100. We use the Adam optimizer to update the inference network. We set the UAV's target region to an elliptical-shaped area where 100 vehicles are randomly and uniformly distributed on the road, with a direct LoS with the UAV. We set the initial coordinates of the UAV to [15; 15], [80; 80], [15; 80], and m. The UAV's coordinates along the x-axis and y-axis are bound by an elliptical path and the altitude along the z-axis is varied between 100 m and 2 km. Within the elliptical path, the UAV can traverse the trajectory coordinates from a theoretically infinite number of possible paths, distributed as a Poisson point process. consumption is due to the fact that a reduced number of vehicles compete for the UAV's communication and computation resources in PFL. When the vehicle velocity is increased to 80 km/hr and the road length (\(R_{L}\)) is increased to 2 km, it can be noted that PFL with attention mechanism leads to the UAV's battery power consumption being similar to that of PFL at lower vehicle velocity. It is evident from Figure 12 that the UAV power consumption is higher in the conventional FL scenario since all the vehicles transmit their local model hyper-parameters to the UAV. Conversely, in the PFL scenario, the UAV power consumption is marginally lower as only a select number of vehicles from a subset are required to transmit their local model hyper-parameters to the UAV. Additionally, each vehicle generates multiple packets in each TTI. The UAV moves around certain areas, since its coverage range is limited to an elliptical path and it should serve a maximum number of vehicles to increase the fairness index and maximize its reward. With target trajectory prediction, the UAV can arrive at the next set of coordinates from where it can be in the LoS of the maximum number of vehicles. From the trend in Figures 13 and 14, when using PFL with attention mechanism, we observe a reduction of approximately 20% in the UAV's transmit power and an approximate 15% time reduction  
Figure 14: Variation in UAV’s transmit power (dBm) with \(V\) for varying vehicle speed and road length (\(\mathcal{R}_{L}\)) for PFL and PFL with attention mechanism scenarios over model training time slots. Figure 13: Variation in UAV’s transmit power (dBm) with \(V\) for varying vehicle speed and road length (\(\mathcal{R}_{L}\)) for FedSGD and PFL with attention mechanism scenarios over model training time slots. in model convergence. For a similar environment, PFL offers a reduction of 15% in the UAV's transmit power and energy or a 10.5% time reduction in model convergence. Hence, using PFL, improved prediction accuracy in the UAV's trajectory leads to an approximate reduction of 15% in the UAV's transmit power, with a 20% reduction in communication overhead from vehicles. Combining PFL with attention mechanism leads to a further reduction in the UAV's transmit power as well as a reduction in the communication overhead from vehicles. Figure 15 illustrates the variation in the UAV's transmit power (dBm) with \(V\) for varying vehicle speed and road length (\(\mathcal{R}_{L}\)) for FedAvg: The FedSGD, PFL, and PFL with attention mechanism scenarios over model training time slots. Figure 15 illustrates the outputs of Figures 12 and 13 on a single plot for comparative analysis. It is observed from Figure 15 that the UAV's average transmit power (dBm) is approximately 14 dBm for FedSGD and 8 dBm for PFL mechanism. This implies an approximate 40% reduction in the UAV's transmit power when using PFL. For PFL with attention mechanism, the transmit power is approximately 10 dBm.