# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Simulation Results
#### 6.4.4 Mobility and Handover Effects  
Figure 10 shows the average test accuracy of single-server FL, cloud-based FL, and MS-FL (DWAA) under three mobility scenarios: none, dropout, and handover. In the none scenario--where all vehicles stay within their original server's coverage--single-server FL  
Figure 9: Test accuracy vs. normalized running time for single-server FL, MS-FL (DWAA), and cloud-based FL with \(\mathrm{t_{c}}=5\) and \(\mathrm{t_{c}}=10\), on (**a**) MNIST and (**b**) GTSRB. achieves 76% accuracy, cloud-based FL 88%, and MS-FL 87%. Introducing a 40% dropout, in which randomly selected vehicles fail to upload their updates, reduces accuracy to 61% for single-server FL, 85% for cloud-based FL, and 83% for MS-FL. When handover is enabled, so that migrating vehicles can successfully transfer their models, accuracy rebounds to 81% for single-server FL and to 91% for both cloud-based FL and MS-FL. Notably, the 95% confidence intervals for cloud-based FL (0.8915-0.9321 under handover vs. 0.8239-0.9382 under none) and MS-FL (0.8875-0.9328 vs. 0.8039-0.9372) overlap almost completely, indicating that both frameworks perform equivalently in the none and handover scenarios. Single-server FL benefits from handover--its CI-high rises from 80% in the none scenario to 85% under handover--because handover enables indirect model sharing across RSUs, which does not occur when vehicles remain within one RSU's coverage throughout the training process. These additional cross-server updates allow the global model to learn more quickly from diverse client data, producing the accuracy gain. Figure 11 plots the test accuracy and loss versus normalized running time on the MNIST dataset for each FL framework under three conditions: none, dropout, and handover. In the none scenario (solid lines), MS-FL with SPAA achieves the highest accuracy and lowest loss, converging in just 20 rounds. In the dropout scenario (40% drop rate), because only 10% of clients participate each round--and those clients are evenly distributed across three RSUs--a 40% dropout corresponds to losing just one client's update per server. As a result, all frameworks experience some performance degradation, but MS-FL with SPAA still converges by round 20 to nearly the same accuracy and loss levels, demonstrating resilience to occasional missing updates. When handover is enabled--so migrating vehicles can successfully upload their models to the new RSU--both MS-FL and cloud-based FL recover performance close to the none baseline. Interestingly, single-server FL also benefits under handover: indirect model exchanges among RSUs improve its convergence compared to the none case, where no inter-server interaction occurs. Figure 10: Average test accuracy under a Dirichlet (\(\alpha\) = 0.1) non-IID split, comparing single-server FL, cloud-based FL, and MS-FL (DWAA) across three mobility scenarios—none, dropout, and handover after 10 communication rounds. Figure 12 plots the test accuracy versus normalized running time on GTSRB (we set the cloud round latency factor to \(\text{t}_{\text{c}}=5\), with edge server latency = 1 unit and computation = 0.1, as detailed in Section 6.4.3). Here, both single-server FL and MS-FL DWAA (plotted for dropout and handover) converge much more quickly than cloud-based FL: the cloud-based curves start at a larger time offset (reflecting the edge-cloud round delay) and take significantly longer to plateau. In contrast, MS-FL DWAA achieves around 0.80 accuracy by time \(\approx\) 100 and nearly 0.85 by time \(\approx\) 200, whereas single-server FL lags behind. Enabling handover (dashed lines) further boosts accuracy, most notably for single-server FL, demonstrating that preserving and forwarding \"lost\" updates can meaningfully mitigate the effects of client mobility. Figure 11: Test performance on MNIST versus normalized running time, comparing single-server FL, MS-FL (DWAA), and cloud-based FL (\(\text{t}_{\text{c}}=5\)) under three conditions—none, dropout, and handover. (**a**) Accuracy over time, (**b**) loss over time.