SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles

Abstract

In transportation, a vehicle's route is one of the most private information. However, to mutually learn some phenomena in a city, for example, parking lot occupancies, we might have to reveal information about it. In this paper, we focus on assessing the privacy loss in a vehicular federated machine learning system. For the analysis, we used the Monaco SUMO Traffic Scenario (MoST). We also used the simulation inputs as statistical data to calculate privacy loss metrics. Results show that a vehicular federated machine learning system may pose a smaller privacy threat than individual learning, but its performance is lower compared to a centralized learning approach. Due to the vast amount of data and processing time, we also describe a method to build a Docker image of SUMO together with a software client-server architecture for SUMO-based learning systems on multiple computers.

Keywords

• federated learning
• communicating vehicles
• efficiency
• security