# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Simulation Results
#### 6.4.2 Comparative Experiments: MS-FL vs. Baselines  
To evaluate and compare three FL architectures--single-server FL, cloud-based FL, and our proposed MS-FL--we distributed each client's data by sampling class proportions from a Dirichlet distribution (concentration parameter \(\alpha\)) and assigning images accordingly. Figure 6 shows the average test accuracy on MNIST after 5, 10, and 15 communication rounds. Across all rounds, MS-FL consistently outperforms single-server FL, and closely tracks cloud-based FL. While cloud-based FL retains a small accuracy lead---93% vs. -91% at 15 rounds--MS-FL avoids the additional communication with the cloud server for every training round, thereby saving communication bandwidth and reducing transmission latency.  
Figure 5: Ablation of SPAA’s outlier penalty on the GTSRB dataset (Dirichlet split, \(\alpha\)= 0.1). Test accuracy versus normalized time units (one unit = one communication round), averaged across three RSUs: (**a**) over 50 rounds, (**b**) over 200 rounds.  
Figure 7 shows test accuracy, precision, recall and F1-score for the three FL frameworks after five and ten communication rounds (five runs, 95% CI). At both checkpoints, MS-FL dramatically outperforms the single-server baseline--improving every metric by roughly twenty percentage points--and matches the performance of cloud-based FL within its confidence intervals. The gap between MS-FL and cloud-based FL is consistently small (around one to three percentage points), demonstrating that MS-FL achieves near-cloud accuracy while avoiding per-round global aggregation.  
Table 4 reports the average performance of the three FL frameworks on the GTSRB dataset (Dirichlet non-IID split, \(\alpha=0.1\)) using a CNN model after 50 communication rounds. Each value is averaged over the three servers. Our MS-FL framework with DWAA achieves the highest accuracy (73.29%), precision (62.32%), recall (62.26%), and F1-score (60.45%), compared to cloud-based FL and single-server FL.  
Figure 8 extends this comparison to 200 communication rounds, averaged over 30 independent runs (95% CI). After 200 rounds, cloud-based FL marginally outperforms MS-FL in all four metrics, but it incurs significantly higher transmission latency (as shown in Figure 7). In contrast, MS-FL with DWAA converges faster, achieving near-peak performance well before 200 rounds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Different FL**} & \multicolumn{4}{c}{**Performance Evaluation Parameters**} \\ \cline{2-5}  & **Test Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline MS-FL DWAA & 0.732858 & 0.623197 & 0.622598 & 0.604465 \\ \hline Cloud-based FL & 0.729137 & 0.600695 & 0.62328 & 0.595817 \\ \hline Single-server FL & 0.658802 & 0.549469 & 0.560228 & 0.523089 \\ \hline \end{tabular}
\end{table}
Table 4: Performance evaluation on GTSRB dataset (non-IID split via Dirichlet, \(\alpha=0.1\)) with a CNN model after 50 communication rounds.  
Figure 6: Average test accuracy (%) on the MNIST dataset (non-IID split with Dirichlet \(\alpha=0.1\)) after 5, 10, and 15 communication rounds under three FL frameworks—single-server FL, the proposed MS-FL with DWAA, and cloud-based FL. Error bars denote the 95% confidence interval across five independent runs.