# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 1 Introduction
While the server plays a critical role in orchestrating the learning process by aggregating local model updates and ensuring data privacy and system security, the potential of multi-server architectures--particularly when explicitly considering server performance metrics such as accuracy and loss--remains underexplored. Given the rising prevalence of delay-sensitive applications, especially in autonomous driving and wearable health monitoring, recent studies have proposed multi-server FL architectures. To effectively reduce the communication latency inherent in traditional FL, two primary multi-server approaches have been explored: (1) hierarchical FL (HFL) [10; 11; 12; 13] and (2) clustered FL (CFL) [14; 15; 16; 17]. HFL adopts a layered structure in which multiple edge servers independently aggregate local model updates from clients within their coverage areas. These edge servers then forward their aggregated models to a central cloud server for global aggregation. However, as model exchanges between edge servers and the cloud server are still necessary, HFL may still experience substantial training delays, especially when the propagation delay between edge servers and the cloud server is considerable. CFLpartitions clients into distinct clusters, each training a separate machine learning model. Nevertheless, dynamic re-clustering may be required across multiple communication rounds, significantly increasing computational complexity and overall training time [18, 19]. In this paper, we present a novel MS-FL framework specifically designed for vehicular networks, aligning seamlessly with the vehicular edge computing (VEC) architecture by leveraging RSU resources to accelerate ML model training. In this framework, FL servers deployed at RSUs collaboratively exchange and evaluate trained models using multicast communication, thereby reducing the network overhead. This collaboration enables quality-driven model exchange, granting greater weight to more reliable contributions and thus accelerating global convergence. The contributions of this paper can be summarized as follows:  
* We propose a novel multi-server framework at the RSU level, enabling peer servers to share model updates among neighboring servers. By leveraging inter-RSU collaboration, our framework accelerates convergence and enhances robustness in highly mobile vehicular networks;
* We develop and evaluate server-level, performance-based aggregation strategies whereby each FL server first assesses incoming peer models' accuracy and loss against its own validation data and then selectively incorporates these updates. Empirical results demonstrate that these performance-driven methods consistently outperform standard baselines. We also employ a statistical model to penalize outliers and reduce the impact of contributions from servers whose data or model updates deviate significantly from the expected behavior. These deviations, or \"outliers\", may indicate malicious activity, faulty data, or other anomalous behaviors;
* We propose an inter-server handover mechanism for continuous FL that preserves updates that would otherwise be lost as vehicles traverse multiple servers, leveraging migrating clients to accelerate global convergence. Our approach also incorporates a server-side evaluation module that assesses and weights each newly arrived client's update--taking into account its origin server--before integrating it into the global model aggregation;
* We show via experiments that our solution provides remarkable performance gains compared to (1) baseline FL at each server, where servers train and aggregate only their own local updates (no inter-server collaboration), referred to as \"single-server FL\" in this paper; (2) hierarchical FL with cloud synchronization, in which servers periodically send models to--and receive updates from--a central cloud server (incurring a high communication overhead), referred to as \"cloud-based FL\" in this paper; and (3) methods that do not take model handover between servers into account.