# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 1 Introduction  
Autonomous driving (AD) combines advanced sensing, high-performance computing, and artificial intelligence (AI) to perceive the environment and make control decisions without human intervention, substantially reducing the number of collisions caused by drivers' errors. At its core, machine learning (ML)--especially deep neural networks--powers essential autonomous functions such as object detection, semantic segmentation, and path planning by extracting rich, hierarchical features from camera, LiDAR, and RADAR data [1]. Modern vehicles are estimated to generate enormous volumes of raw sensor data--up to one gigabyte per vehicle per second [2]--and are increasingly interconnected via vehicle-to-everything (V2X) links [3]. As connected vehicles become more prevalent, vehicular operations are expected to rely not only on locally collected data, but also on sharedsensor data across networks of interconnected vehicles [3]. Future autonomous vehicles will perceive their environment through built-in sensors and share this information with other vehicles via wireless communications, collecting substantial amounts of data for distribution [1]. Sharing sensor data is critical for safety applications, such as creating high-definition maps, and for developing ML models that perform autonomous driving tasks like speed adjustment, steering, and traffic sign recognition [4]. Traditionally, vehicular networks have relied on a centralized learning (CL) paradigm: raw sensor outputs are uploaded to a cloud, where powerful ML models--typically deep neural networks--are trained to map inputs (e.g., images, point clouds) to driving outputs (e.g., steering angles, object classes), and the resulting global model is then pushed back to vehicles for on-board inference. However, CL's dependence on continuous, large-scale data transmission can overwhelm the network bandwidth, introduce latency that undermines safety-critical response times, and expose sensitive sensor data [1]. The widespread distribution of sensor data also raises serious privacy concerns, as it can reveal confidential information about vehicles and occupants. While privacy issues related to sharing vehicle status data have been addressed through methods like data anonymization, these measures have not been fully applied to sensor data sharing [3]. Moreover, conventional data anonymization techniques have seen limited application to high-volume, high-fidelity sensor streams, leaving significant privacy gaps. To mitigate privacy concerns, federated learning (FL) [5; 6] has emerged as a promising solution, enabling clients (e.g., vehicles) to collaboratively develop ML models without sharing confidential raw data. In FL, a server shares the initial model parameters with clients, who train the model on local datasets and return updated parameters. The server aggregates these updates to create a global model, iterating until a predefined level of accuracy is achieved. Since FL is trained on distributed datasets and typically involves numerous communication rounds between clients and the central server to exchange model updates, two major challenges have emerged and attracted significant research focus: improving communication efficiency between clients and the central server and addressing the heterogeneous distribution of local datasets across clients [6]. Existing research on FL has traditionally focused on single-server architectures, where clients repeatedly exchange model updates with a centralized server during each communication round. This interaction strategy can introduce significant communication delays, especially in large-scale FL scenarios where numerous clients may be geographically distant from the central server--delays that are exacerbated when the central server is deployed in the cloud. Moreover, real-world vehicular networks often involve multiple access points, each with its own server, yet previous studies [7; 8; 9] on FL in vehicular networks have primarily emphasized client-side operations.