## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### System Evaluation
#### 5.2.3 Analysis of the Advantages Versus Centralized Approach  
In implementing this use case, it is crucial to consider the impact of, both centralized and federated approaches on bandwidth utilization and convergence speed. To facilitate this comparison, it is important to examine the information transmitted in each approach. The centralized ML approach involves transmitting sensitive information within the observation and action vectors, whereas the FL system transmits only the neural network parameters of the client model to the aggregation server, as illustrated in Figure 15. Similarly, for the system outlined in this paper, the aggregation cloud would correspond to the aggregation server based on the Flower framework that is deployed as an rApp in the non-RT RIC.  
To illustrate this point, consider the use case outlined in this section for the centralized approach. Assuming each agent transmits 500,000 samples (comprising observations and actions, as determined from the centralized stage training results presented in [7]) to the server for training, we can calculate the bandwidth utilization. Table 7 presents the bandwidth utilization for this scenario, including the size of the observation vector (assuming the observation vector consists of seven elements of type _float32_, each occupying 4 bytes) and the action vector, for one and four clients.  
Figure 14: Results of joint evaluation in the use case.  
Conversely, the federated approach requires each client to transmit only the neural network parameters to the aggregation server. Table 8 presents the bandwidth utilization of the federated system for this specific use case, assuming each client sends 100,000 samples (model parameters, with a worst-case size of approximately 1 MB) per round to the aggregation server. It is further assumed that three rounds are needed to achieve convergence based on the results from the federated stage. To enhance security, Transport Layer Security (TLS) was enabled for client-server communications. This included the TLS header in the transmitted data, increasing the total data size by 10%.  
In summary, for this specific use case, the centralized ML approach would transmit 64 MB, whereas the FL approach would transmit only 26.4 MB while also incorporating an additional security layer. Moreover, the convergence rate of the learning process highlights improved efficiency in the federated approach. Figure 16 compares the global model formed after the third round in the use case presented with the centrally trained model from [7]. The early convergence of the federated system is clearly evident, emphasizing the effectiveness of this method in such scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Per Client**} & \multicolumn{2}{c}{**For 4 Clients**} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Centralized \\ system \\ \end{tabular} } & Size Observation & Size Action & \multirow{2}{*}{Total} & \multirow{2}{*}{Total} \\  & vector & vector & & \\ \cline{2-2} \cline{5-5}  & 14 MB & 2 MB & 16 MB & 64 MB \\ \hline \hline \end{tabular}
\end{table}
Table 7: Bandwidth utilization in the Centralized approach.  
Figure 15: Centralized ML vs FL.