Federated Learning with Infrastructure Resource Limitations in Vehicular Object Detection

Object detection plays an essential role in many vehicular applications such as Advanced Driver Assistance System(ADAS), Dynamic Map, and Obstacle Detection. However, object detection under the traditional centralized machine learning framework, where images transmission utilization of infrastructure resources and privacy concerns about sensitive image content leakage. We introduce Federated Learning, a practical framework that enables machine learning to be conducted in a distributed manner and potentially addresses the traditional centralized machine learning issues by avoiding raw data transmission. However, Federated Learning distributes the pieces of training to the client, which relies on client communication in Vehicular Networks heavily, and not all the clients have the same resources in the real world. Therefore, we study communication and client resource limitation issues where clients have different amounts of local images and compute resources in the Vehicular Federated Learning framework, propose an algorithm to deal with these issues, and design the experiments to prove it. The experimental results show the efficacy of the proposed algorithm, which maintains the object detection precision while improving the 66% training time and reducing 35% communication cost.