## 8 Performance Evaluation
### Discussion and Open Challenges  
The section discusses the potential challenges of the scheme. The same are discussed as follows:  
1. _6G channel characteristics_: The presented scheme considers the deployment of a UAV swarm setup, where they are controlled through a controller node. In such cases, we considered 6G communication networks for effective UAV set up, where we analyze the performance based on 6G frequency and bandwidth ranges on the MATLAB simulation tool. However, in practicality, the performance and robustness would depend significantly on the size of the UAV swarm network and the fading model estimation. Moreover, the lack of open standards for 6G communication channels hinders the accurate modeling of the UAV setups. Thus, an open challenge is the standardization of 6G standards, which would allow homogeneity in the overall communication process.
2. _Accuracy of non-stationary FoR estimation_: Moreover, we considered that objects are in non-stationary FoR, and thus the object detection accuracy (estimation of the bounding boxes) significantly depends on the velocity of the object. In our simulation, we considered the YOLOv3 model owing to its improved detection accuracy in real time. The algorithm achieves a fine balance between the detection speed against accuracy, which is essential for non-stationary FoR, but the inherent model complexity adds overhead to the overall model. In YOLOv3, a convolutional neural network is formed, where the location and class information of the object is fed as input. A deep feature map is formed, and in our simulations, we experienced a significant loss in spatial features. Thus, the person localization in moving frames has low accuracy compared with stationary FoR. In such cases, a future approach is moving towards shallow feature maps, which form a higher response towards the spatial features and improve the model accuracy.
3. _Challenges in FL setups_: In our approach, we considered that edge nodes (\(E_{ser}\)) deploying local FL models and communicating the gradients to the global cloud server. The setups and simulations are promising for small setups, but in real life, a massive number of edge nodes are required to realize our 3 FFs operations in a wide range. Moreover, the UAV swarm network controller nodes would increase, and thus, the local computations would become expensive. In such cases, a potential challenge is to address an optimized communication model that sends the updates (gradients) efficiently. In simulations, owing to a small setup, we ignored the communication overheads in the FL communications. Secondly, FL devices (smartphones and laptops) have limited computing capability, and the increase in network size requires low-powered protocols at the network and transport layers. In such cases, hybrid approach (both connectionless and connection-oriented) link setups are required, which can be fine-tuned with software emulation functions in the 6G networks. At last, the data under consideration in our simulations have fewer data-point variations, and thus preserve the non-identically distributed (non-id) considerations. In large setups, the statistical heterogeneity might violate the frequency distributions, which increases the training time of the overall network. The loss optimization at the global model is slower in such cases.