Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD system), conventional models often struggle with under-fitting as the amount of accumulated training data progressively increases. To address this issue, instead of conventional small models, employing Large Vision Models (LVMs) in FedAD is a viable option for better learning of representations from a vast volume of data. However, implementing LVMs in FedAD introduces three challenges: (I) the extremely high communication overheads associated with transmitting LVMs between participating vehicles and a central server; (II) lack of computing resource to deploy LVMs on each vehicle; (III) the performance drop due to LVM focusing on shared features but overlooking local vehicle characteristics. To overcome these challenges, we propose pFedLVM, a LVM-Driven, Latent Feature-Based Personalized Federated Learning framework. In this approach, the LVM is deployed only on central server, which effectively alleviates the computational burden on individual vehicles. Furthermore, the exchange between central server and vehicles are the learned features rather than the LVM parameters, which significantly reduces communication overhead. In addition, we utilize both shared features from all participating vehicles and individual characteristics from each vehicle to establish a personalized learning mechanism. This enables each vehicle's model to learn features from others while preserving its personalized characteristics, thereby outperforming globally shared models trained in general FL. Extensive experiments demonstrate that pFedLVM outperforms the existing state-of-the-art approaches.

翻译：基于深度学习的自动驾驶模型常因环境领域漂移中的数据异质性而导致泛化能力差。尽管联邦学习能提升自动驾驶模型的泛化能力（即FedAD系统），但传统模型常因累积训练数据量逐渐增大而面临欠拟合问题。为解决该问题，在FedAD中采用大视觉模型替代传统小模型，是从海量数据中更好学习表征的可行方案。然而，在FedAD中部署大视觉模型面临三大挑战：（I）在参与车辆与中心服务器间传输大视觉模型产生的极高通信开销；（II）每辆车上缺乏部署大视觉模型所需的计算资源；（III）大视觉模型聚焦共享特征而忽视本地车辆特性导致的性能下降。为克服这些挑战，我们提出pFedLVM——一种大视觉模型驱动、基于潜在特征的个性化联邦学习框架。该方法中，大视觉模型仅部署在中心服务器上，有效减轻了各车辆的计算负担。此外，中心服务器与车辆间交换的是学习到的特征而非大视觉模型参数，显著降低了通信开销。同时，我们利用所有参与车辆的共享特征和每辆车的个体特性建立个性化学习机制，使各车辆模型在保留自身个性化特征的同时学习其他车辆的特征，从而优于常规联邦学习训练的全局共享模型。大量实验证明，pFedLVM的性能超越了现有最先进方法。