## 6 Simulation Results and Discussion
### Variation in Average Packet Drop Rate with Control Parameter (\(\varrho\)) Using Fed-DDPG  
The variation in the average packet drop rate with control parameter (\(\varrho\)) using fed-DDPG is illustrated in Figure 9 for \(V=1\)-100. As we increase \(\varrho\), the model convergence improves, and the average packet drop rate gradually reduces as \(\varrho\) is increased from 2000 to 10,000. Increasing \(\varrho\) beyond 10,000 increases the required number of iterations and hence increases the latency. Moreover, the transmit power of UAV increases with the increase in the number of vehicles and with the increase in the UAV trajectory fluctuations. In the Rician channel model, the channel gain has a non-monotonic relationship with the flying altitude of the UAV. While increasing the flying altitude improves the channel conditions for certain vehicles and leads to improved throughput, it simultaneously requires higher transmission power to meet the QoS requirement. The results reveal that as \(\varrho\) increases, the proposed solution optimizes the task execution time to reduce the queue backlog to ensure queue stability. As the value of \(\varrho\) decreases, the priority of the queue backlog increases with the reduced computation rate.  
The variation in the average UAV energy with the number of vehicles at a specific UAV altitude (\(\mathcal{H}\)) for different packet transmission sizes is illustrated in Figure 10 for the FL model transmission, BSM, and CPM packets. Here, we vary the packet size from  
Figure 8: Total delay (\(\mathcal{D}\)) vs. number of vehicles (\(V\)) for different machine learning models.  
Figure 9: Variation in average packet drop rate with control parameter (\(\varrho\)) using fed-DDPG.  
1 to 5 MB. NLoS propagation conditions and path losses are increased in dense urban environments due to obstacles such as buildings, trees, and other structures that follow a Rayleigh distribution. For vehicles to achieve their target SINR thresholds in dense urban areas, the UAV needs to fly at higher altitudes and transmit data at a higher power.  
Variation in FL Computation Rate and Average UAV Energy with \(V\) for Different Machine Learning Models  
Here, we consider the effect of wind in the direction of UAV hovering, as well as in the direction against UAV hovering. We add a random SNR to \(\mathbf{q}_{(x,y)}\) directions of the UAV's position, and \(\mathcal{H}\), respectively. We execute this iteration for a set of changes in the number of layers and the number of hidden nodes in LSTM. It is observed that when the number of hidden nodes exceeds 32, the average MSE value increases significantly, so it is not necessary to have more than 32 hidden nodes. For scenarios with 1-100 vehicles, due to the limited available resources, not all vehicles achieve their required SINR. Hence, the UAV increases its flying altitude and hence power consumption to provide service to these vehicles to reduce packet drops and retransmissions.  
As a result, the UAV consumes more energy and has a shorter flight time since it starts from the coordinate origin and does not return to its starting point when it finishes the coverage task. Moreover, when the vehicle data size is smaller, we achieve a higher computation rate for the fed-DDPG algorithm. However, when the vehicle data size increases, the transmission task is completed in a shorter amount of time, and the performance gain increases. Increasing the number of vehicles increases the average UAV power consumption, as the bandwidth available to each vehicle decreases. The variation in FL computation rate (Mbits/s) with control parameter (\(\varrho\)) for different machine learning models is illustrated in Figure 11.  
Figure 10: Variation in average UAV energy with number of vehicles (\(V\)) for different machine learning models.