## 5 Simulation Results and Discussion
### Variation in UAV's Transmit Power (dBm) with Number of Vehicles (\(V\))
This slight increase in the UAV's transmit power as compared to PFL is due to the fact that a portion of the UAV's power is dissipated in selecting a vehicle for local model hyper-parameter transmission from a subset of vehicles. This selection is based on the attention weights that allow the UAV to identify a vehicle with dominant local model hyper-parameters to be transmitted from vehicles to the UAV. Although this difference in the two approaches, i.e., PFL and PFL with attention mechanism, is negligible over a smaller road length, this difference may be larger and significant in real-world traffic scenarios. Variation in Average Mean Square Error (MSE) Losses with Personalization Parameter (\(\varphi\)) Using PFL and PFL with Attention Mechanism  
The variation in normalized MSE losses with number of training episodes for the PFL and PFL with attention mechanism scenarios is illustrated in Figures 16 and 17 for \(V=1\)-100. We evaluate the effectiveness of the proposed PFL model aggregation approach. The UAV's transmit power and bandwidth limits are considered along with the vehicles' wireless resource limitations. As the selected vehicles upload their local model hyper-parameters for aggregation, PFL requires more computation and bandwidth resources than the PFL approach with attention mechanism. It is observed from the model convergence characteristics that for the available battery power at the UAV, the MSE of PFL  
Figure 15: Variation in UAV’s transmit power (dBm) with \(V\) for varying vehicle speed and road length (\(\mathcal{R}_{L}\)) for FedAvg, FedSGD, PFL, and PFL with attention mechanism scenarios over model training time slots. is reduced by approximately 6.75% for \(V=50\), by 8.5% for \(V=100\), and by 12.5% for PFL with attention mechanism, in comparison with the conventional FL approaches. For the UAV's optimal trajectory prediction with PFL and PFL with attention mechanism on the _LTE I/Q_ dataset, the MSE is reduced by 9.5% for \(V=50\), 5.5% for \(V=100\), and 3.75% for PFL with attention mechanism, in comparison with the conventional FL approaches. The above results demonstrate the superior performance of our proposed PFL method. Here, we set the learning rate to 0.001 and utilize the Adam optimizer to update the model in subsequent iterations. To verify the advantages of the PFL with attention mechanism, we compare its performance with PFL to regularize each local loss function and to address the data heterogeneity. Moreover, in PFL, the selected vehicles upload the entire model to the UAV for aggregation in each round. In each round, the selected vehicles sequentially train the feature extractor and predictor. Hence, the PFL algorithm converged after approximately 50 iterations, whereas PFL with attention mechanism converged after approximately 25 iterations. We selected 60% of the vehicles such that at least one vehicle was selected from each cluster. The personalization parameter was varied for other vehicles in a cluster. We tuned each vehicle's personalization parameter to select between the cluster-wise embedding vectors with lower dimensionality and to train the model in fewer iterations. We also verified that the tunable parameter to balance the training performance and power consumption of the UAV with a varying number of vehicles impacts the average delay. As illustrated in Figure 18, as the number of vehicles and vehicle velocity increases, the vehicles consume more energy, resulting in scheduling more data transmissions to the UAV. On the V2X-Sim dataset, the PFL results in 4.50% and 3.50% improvement in delay for \(\varphi=0.001\) and \(\varphi=0.01\), and obtains a slight improvement in delay when \(\varphi=0.1\) compared with the traditional FL algorithms. The simulation results reveal that the proposed approach can schedule fewer data samples and still achieve higher learning accuracy. The performance gain is derived from the joint optimization of vehicle selection, bandwidth allocation, communication and computation time, and TTI allocation policies.