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.

翻译：联邦学习（FL）已成为一种在保护数据隐私的前提下，利用分散数据训练机器学习模型的有效方法。本文提出了一种面向质量检测任务中目标检测的联邦学习算法，采用YOLOv5作为目标检测算法，联邦平均（FedAvg）作为联邦学习算法。我们将该方法应用于一个制造场景，其中多个工厂/客户在非独立同分布（non-IID）数据集上贡献数据，以训练全局目标检测模型，同时保障数据隐私。实验结果表明，与仅使用本地客户数据集训练的模型相比，我们的联邦学习方法在所有客户的测试数据集上实现了更好的泛化性能，并生成了更优的目标边界框。本工作展示了联邦学习在制造业质量检测任务中的应用潜力，并为利用YOLOv5和FedAvg实现联邦目标检测的性能与可行性提供了宝贵见解。