  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.
