# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 7 Discussion and Future Directions  
To enhance privacy in autonomous vehicle machine learning, this paper proposed a novel MS-FL framework designed specifically for the data heterogeneity and mobility of autonomous vehicle networks. By enabling neighboring servers to exchange model updates directly--rather than routing everything through a central cloud--MS-FL achieves faster convergence and superior predictive performance compared to both single-server FL and hierarchical edge cloud-based FL. Crucially, MS-FL edges out the cloud-based approach without requiring any RSU-cloud communication, slashing end-to-end latency and conserving backbone bandwidth. Our MS-FL framework overcomes the limitations of prior edge-only and cloud-based schemes through two core innovations. First, at the server level, each server evaluates peer models on its own validation data and aggregates them using performance-driven strategies (BA, SA, DWAA, and SPAA), ensuring that only high-quality contributions shape the global model and avoiding blind averaging under non-IID data. Second, MS-FL enables seamless handover with lost update preservation: when vehicles migrate between coverage areas, their local updates are retained and forwarded to the next server, preventing wasted training effort and maintaining the continuity of learning. By sharing and weighting these high-quality updates from diverse locations--filtering out outliers and stale models before they can degrade performance--MS-FL accelerates convergence in highly heterogeneous vehicular environments. Under a \"dropout\" failure scenario--where migrating vehicles fail to upload their updates--enabling handover speeds up convergence in MS-FL, particularly during the early communication rounds. Introducing handover into single-server FL produces even larger relative gains, as it facilitates the indirect exchange of model updates among servers and significantly improves its convergence speed. Across both shard-based and Dirichlet-based non-IID data splits--and under both dropout and handover mobility scenarios--MS-FL consistently delivers superior accuracy, precision, recall, and F1-scores compared to vanilla FL. SPAA, in particular, shines by dynamically weighting updates according to their statistical reliability, while DWAA and SA also significantly outperform the baseline, demonstrating that even simple performance-based filtering can yield substantial gains. For future work, we will extend our evaluation to real-world mobility scenarios, using driving simulators to measure end-to-end performance under dynamic RSU handovers,  
Figure 12: Test accuracy versus normalized running time on the GTSRB dataset, comparing single-server FL, MS-FL (DWAA), and cloud-based FL (\(\text{t}_{\text{c}}=5\)) under dropout (solid lines) and handover (dashed lines) scenarios. variable link latency, and evolving data streams. We will also assess MS-FL's resilience to adversarial threats--such as model poisoning and inference attacks--and develop incentive mechanisms to ensure sustained client participation, which is essential for fairness and scalability in federated settings. Finally, incorporating privacy-preserving techniques like differential privacy or secure aggregation will further safeguard user data, laying the groundwork for efficient, secure, and scalable FL in next-generation connected vehicles. Conceptualization, S.S.H. and K.E.-K.; methodology, F.M., S.S.H. and K.E.-K.; software, F.M. ; validation, F.M., S.S.H. and K.E.-K.; formal analysis, F.M. ; investigation, F.M. ; resources, S.S.H. and K.E.-K.; data curation, F.M. ; writing--original draft preparation, F.M. ; writing--review and editing, F.M., S.S.H. and K.E.-K.; visualization, F.M. ; supervision, S.S.H. and K.E.-K.; project administration, S.S.H. ; funding acquisition, S.S.H. and K.E.-K. All authors have read and agreed to the published version of the manuscript. NSERC Grant RGPIN/004408-2019. The original contributions presented in this study are included in the article.