## 5 Simulation Results and Discussion
### Performance Comparison of PFL with Traditional FL Methods  
In this section, we compare the performance of our proposed PFL approach with FedAvg and FedSGD for a varying number of vehicles (\(V\)) and varying road lengths (\(R_{L}\)). We assume a scenario with 100 vehicles. Our algorithm converged after 30 iterations and processed 100% of the local model hyper-parameter updates from all the vehicles in the FedAVg and FedSGD scenarios. However, as all the vehicles communicated their local model hyper-parameter updates to the UAV, there was a notable delay in receiving the global model hyper-parameter from the UAV. Using PFL, only a subset of vehicles in each TTI communicated their local model hyper-parameter updates to the UAV, resulting in lower latency and sojourn time. The parameter _percentage of latency violations_ represents the percentage of instances where the round-trip latency exceeds a predefined threshold. The benchmark predefined threshold values are specified and periodically updated in the 3 rd Generation Partnership Project (3 GPP) specifications [1; 92; 93]. However, in our simulated environment, because of limited computing power, we did not set the threshold to a lower value between \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Vehicle Mobility & Manhattan Mobility \\ \hline Number of vehicles (\(V\)) & 1–100 \\ \hline Number of \(\mathcal{PS}\) in UAV & 1 \\ \hline UAV deployment altitude & 100 m–2 km \\ \hline Elliptical path’s major axis & 300 m–1000 m \\ \hline Elliptical path’s minor axis & 150 m–550 m \\ \hline Edge server location & In vehicle \\ \hline Communication frequency & 5.9 GHz \\ \hline Modulation technique & OTFS \\ \hline Distance between vehicles & 10–100 m \\ \hline Road length (\(R_{L}\)) & 1–4 km \\ \hline Vehicle speed & 0–100 kmph \\ \hline Mean speed of vehicles & 50 km/h \\ \hline Standard deviation in vehicle speed & 10 km/h \\ \hline Packet size for gross data offloading & 1 byte–3 megabytes \\ \hline Packet size of FL models & 1 byte–10 megabytes \\ \hline Dataset used & V2X-Sim, LTE I/Q \\ \hline Inter-arrival time for packets at UAV & 100 ms–1000 ms \\ \hline OTFS base station transmit power & 40 dBm (10 W) \\ \hline UAV transmission power & 20 dBm (100 mW) \\ \hline UAV receiving threshold & \(-80\) dBm \\ \hline Vehicle transmission power & 25 dBm (316.2 mW) \\ \hline Noise power & \(-50\) dBm (\(10^{-8}\) W) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. 2 ms and 5 ms, as appears in some 3 GPP specifications. In our simulations, the maximum delay in ms was around 30 ms for the FL, FedAvg, and PFL approaches. So, we set the latency threshold (\(L_{max}\)) as 30 ms, and for any instance when the delay, comprising queuing delay plus processing delay, exceeded 30 ms, we assumed the packet was either dropped or retransmitted. In an iteration, if the delay exceeds the \(L_{max}\) of 30 ms, we consider that as an instance of latency violation. As the number of vehicles was increased, a larger number of packets exceeded the latency threshold \(L_{max}\) of 30 ms for the queuing delay and processing delay metrics; this is plotted as the percentage of latency violations in Figures 5 and 6. It was noted that the percentage of latency violations was around 30% for PFL and 15% for PFL with attention mechanism. The 50% reduction in the percentage of latency violations for PFL with attention mechanism validates the advantage of using attention weights and utilizing dominant states for UAV trajectory prediction. In PFL, a minor increase in delay is observed compared to PFL with attention mechanism to ensure that the UAV's processing capacity is not exceeded and the queue does not overflow. The proposed PFL approach ensures that whenever the latency threshold constraint is breached, the next set of arriving packets are dropped. If the UAV's queue or buffer becomes available in this TTI, the dropped packets are retransmitted. Otherwise, the next set of packets is transmitted in the next TTI.