Poster: Towards Realistic Federated Learning Evaluations for Connected and Automated Vehicles

Yongkang Liu, Chianing Wang, Kentaro Oguchi

Abstract: Federated learning (FL) is widely recognized as a valuable approach for Connected and Automated Vehicles (CAVs) because it facilitates collaborative model development across a multitude of vehicles in a decentralized manner. However, numerous studies on FL algorithms only assessed their performance through experiments conducted in simulated client-server configurations (e.g., where both server and clients run on the same machine) or simplified scenarios that do not account for client downtime. In this paper, we aim to conduct more realistic evaluations for CAV applications leveraging FL. We present a preliminary experimental study as well as offer insights into potential future directions.

External IDs:doi:10.1145/3583740.3626619