Enabling Federated Learning for Object Detection in Connected Autonomous Driving Using YOLO with the Flower Framework

Connected autonomous vehicles (CAVs) rely on object detection models to ensure safe and efficient navigation. Traditional centralized training approaches pose challenges related to data privacy, scalability, and communication overhead. In this study, we integrate Federated Learning (FL) with YOLO models, including YOLOv5, YOLOv8, and YOLOv11, for object detection in CAVs, utilizing the Flower framework to enable decentralized training while preserving data privacy. We design a virtual client setup that replicates a realistic scenario and apply FedAvg and FedProx aggregation strategies on the KITTI and BDD100K datasets. Our experimental results demonstrate that FL-based training outperforms traditional centralized learning, with YOLOv8 achieving a mean average precision (mAP) of 87.9% in KITTI and 61.5% in BDD100K, outperforming the baseline. Our study highlights the feasibility and effectiveness of deploying FL-based object detection models in CAVs, by conducting a comprehensive evaluation of using the Flower federated learning framework and addressing privacy concerns through decentralized training.