Federated Object Detection for Quality Inspection in Shared Production
This work addresses data privacy challenges in manufacturing quality inspection, but it is incremental as it applies existing methods (YOLOv5 and FedAvg) to a new domain.
The paper tackles the problem of training object detection models for quality inspection in manufacturing using federated learning to preserve data privacy across multiple factories, achieving better generalization and improved bounding boxes compared to local training.
Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose a FL algorithm for object detection in quality inspection tasks using YOLOv5 as the object detection algorithm and Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a manufacturing use-case where multiple factories/clients contribute data for training a global object detection model while preserving data privacy on a non-IID dataset. Our experiments demonstrate that our FL approach achieves better generalization performance on the overall clients' test dataset and generates improved bounding boxes around the objects compared to models trained using local clients' datasets. This work showcases the potential of FL for quality inspection tasks in the manufacturing industry and provides valuable insights into the performance and feasibility of utilizing YOLOv5 and FedAvg for federated object detection.