## 8 Performance Evaluation
### Computational Offloading and FL Characteristics
#### 8.3.2 Result Analysis  
As discussed in Section 5, we designate a decision variable \(D(s)\) which might result in three scenarios of local execution (UAV itself, and hence no offloading is required). In this case, the computation tasks \(T_{p}\) are processed locally (suitable for lightweight tasks). In the case of data-intrinsic tasks, the data collected from UAVs are sent to the controller node, which offloads the data with a 6G-ERLLC link to \(E_{ser}\). At \(E_{ser}\) (edge), tasks are divided and executed with the FL algorithm. Heavy tasks are sent to \(E_{cs}\) for execution, which is offloaded from \(E_{ser}\).  
For local execution, we follow the approach as presented by authors in [58]. Figure 9a presents a comparative analysis of response time when task size varies. As more UAVs send data in batches, the task size increases, and thus the local execution shifts to edge execution, which shifts to cloud execution. Thus, offloading time is added to the response time, which is evident from the figure. As an example, for a task size of 100 MB, the cloud execution time is 8634 ms, edge execution time is 6789 ms, and local UAV execution is 6011 ms. Thus, at the edge, an offloading time of 778 ms is added, and from \(E_{ser}\) to \(E_{cs}\), the additional time of 1845 ms is recorded. The time between edge-cloud becomes further intensified owing to the FL gradient computation and parameter communication. Thus, to address this challenge, a future scope might include optimal control over FL accuracy vs. the response time to edge nodes.  
Next, we analyze the impact of the FL learning algorithm on the batch size. We measure the energy dissipation requirements when the FL model is updated in the cloud (global update) against the update at edge nodes (local update). Finally, for small updates in terms of topology and route formation, the UAV swarm nodes run tiny ML algorithms which can be supported on-device, and hence there is no requirement for parameter update. Figure 9b presents these scenarios. As evident, for a batch size of 120 MB, the dissipated energy is 1089 kJ for cloud-based execution, 887 kJ for edge-based execution, and 802 kJ for on-device execution. On average, a significant improvement of 19.59% is recorded when  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline FL algorithm & FedAvg \\ \(\eta\) & 0.001 \\ Batch-size & Min-batch \\ Epochs & 10,000 \\ Log\_interval & 10 \\ Activation & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._  
Figure 9: Data-centric analysis: benefits of task offloading by UAV swarms/controllers to local edge nodes and analysis of FL computation. (**a**) A comparative analysis of response time (cloud-edge–UAV) based on task size. (**b**) A comparative analysis of energy dissipation (global vs. local vs. on-device training). (**c**) Gradient computation size (single-round) based on task size (in bytes).  
edge execution takes place over cloud execution (where the FedAvg algorithm optimizes the training parameters).  
Finally, we analyze the gradient computation delay with respect to local batch size, \(L_{\text{b}}\), where edge nodes use the local learning and loss functions. Figure 9c presents the details. For a single round, we consider data sizes of \(\{512,1024,2048,4096,8192,16384\}\) bytes. Please note that it is not required that data need to be in exact powers of 2, but for simplicity, we enforced the condition. As evident, with increased size, the learning and loss convergence rate increases, and thus the time to compute the optimal gradient increases.