Towards Federated Fleet Learning Leveraging Unannotated Data - Viala Bellander, Alexander; Ghafir, Yazan (My University, 2023)
Abstract The rapid advancement of autonomous driving technology poses new challenges, including the efficient management and use of large volumes of data generated by autonomous vehicles. federated learning, which allows for distributed, on-device learning, has emerged as a potential solution. However, the effectiveness of federated learning in the context of autonomous driving, particularly when faced with scarce or non-existent labelled data, is still an open question. This thesis explores this issue, employing semi-supervised and imitation learning methodologies within the federated learning framework for autonomous driving tasks such as ego-road segmentation and trajectory prediction. This approach deviates from the conventional assumption of abundant labelled data, aiming instead to maximise on-device learning from unlabelled data. While our experiments demonstrate the potential of federated learning in autonomous driving, results indicate that its performance is currently on par with or slightly less effective than traditional methods for the tasks we studied. Furthermore, this research underscores the largely untapped potential of self-supervised learning methodologies within the federated learning framework for autonomous driving. We argue that further exploration in this area could result in significant breakthroughs and contribute to a future where autonomous vehicles can collectively learn without compromising privacy and efficiency.