Federated Semi-Supervised Learning for Object Detection in Autonomous Driving

One of the main challenges in designing deep learning networks for autonomous driving is the lack of labeled data. Recent trends that address this problem involve the use of unlabeled data. In this paper, we propose a unified semi-supervised and federated learning (FL) approach that is designed to offer cost-efficient and practical training of deep learning object detection models for autonomous driving. In our implementation, we assume that each vehicle is given some well-labeled image data which are coupled with unlabeled image data captured by its cameras. Each of the vehicles has a local object detection model, which will be trained leveraging a semi-supervised learning method with both labeled and unlabeled data. The local model parameters are uploaded to a cloud server and aggregated to update a global FL model which in turn is shared with all the vehicles involved. Performance evaluations showed that our proposed approach is a promising solution as it allows continuous training and thus improved performance in autonomous driving.