## 6 Simulation Results and Discussion
### Probability of Optimal Trajectory Prediction  
The variation in the probability of optimal trajectory prediction using LSTM vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) is illustrated in Figure 12 for \(V=1\)-100. In order to predict a sequence of trajectory coordinates, the LSTM uses past positions or features that represent the movement of an UAV, and predicts the future path. As seen from Figure 12, the probability of optimal trajectory prediction decreases with a greater number of vehicles on the road segment. Also, when the UAV altitude is between 1 and 600 m, the probability of optimal trajectory prediction reduces gradually with the altitude. However, when the UAV altitude is between 600 and 1000 m, the probability of optimal trajectory prediction reduces steeply as compared to an altitude between 1 and 600 m. This is attributed to the available battery power, which restricts UAV movement as well as the number of vehicles the UAV can serve with the available SNR. By combining multiple vehicles, the LSTM improves the sum throughput and reduces the trajectory length, thereby improving the trajectory prediction probability.  
The variation in the probability of optimal trajectory prediction using actor critic vs. UAV altitude (\(\mathcal{H}\)) for a varying number of vehicles (\(V\)) is illustrated in Figure 13 for \(V=1\)-100. The actor-critic comprises an LSTM in both the actor and critic layers. Compared to Figure 12 for \(V=1\)-100, the probability of optimal trajectory prediction using actor-critic is lower than that of fed-DDPG using LSTM for the same number of vehicles, UAV transmit power, road segment length, and UAV altitude. This is because when trained on the _LTE I/Q_ dataset for five different UAV altitudes, the fed-DDPG using the LSTM architecture is able to capture complex dependencies in the UAV trajectory samples, and the data processing time is significantly reduced. In the case of actor-critic, when UAV transmits power between 10 and 28 dBm, the trajectory length and sum throughput performance are affected by the number of vehicles and the vehicle speed. This results in slower convergence speed and hence lower probability of optimal trajectory prediction. The probability of optimal trajectory prediction using CNN-LSTM, RNN, and GRU is illustrated in Figures 14, 15, and 16, respectively. As seen, the optimal trajectory prediction declines steeply as compared to fed-DDPG. Also, the CNN-LSTM, RNN, and GRU models are trained for 1,000 episodes, whereas actor-critic and fed-DDPG yield a higher probability in 500 and 250 training episodes, respectively.  
Figure 11: Variation in FL computation rate (Mbits/s) with control parameter (\(\varrho\)) for different machine learning models.  
Figure 12: Probability of optimal trajectory prediction for fed-DDPG (using LSTM) vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) over trials of 250 episodes.  
Figure 13: Probability of optimal trajectory prediction for actor–critic (using LSTM) vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) over trials of 500 episodes.  
Figure 14: Probability of optimal trajectory prediction for CNN-LSTM vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) over trials of 1000 episodes.  
Figure 15: Probability of optimal trajectory prediction for RNN vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) over trials of 1000 episodes.