Federated learning for vehicular coordination use cases

Vehicular coordination and communication tasks are crucial aspects of enabling autonomous driving, guaranteeing safety and efficiency. In our present work, we explore methods for collecting and distributing information among participants by employing collaboratively-built high-definition maps that contain fine-grained contextual data. We leverage a hierarchical federated learning structure and anticipatory onboarding of the maps through a mobility-aware content caching scheme and minimize the delay of data delivery in both subsystems. We provide analytical models built on queuing theory and integer linear programming and evaluate essential system parameters in an emulation testbed. Based on our results, we conclude that we can significantly reduce the delay in delivering timely information to vehicular clients by introducing intermediary layers in the federated learning structure and by pre-loading current map tiles corresponding to vehicle paths.