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）已成为一种在不牺牲数据隐私的前提下，利用分散数据训练机器学习模型的前沿方法。本文提出一种基于联邦学习的目标检测算法，用于质量检测任务，其中采用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制造业场景：多个工厂/客户端在非独立同分布（non-IID）数据集上协作训练全局目标检测模型，同时保护数据隐私。实验表明，与仅基于本地客户端数据集训练的模型相比，我们的联邦学习方法在全体客户端测试集上取得了更优的泛化性能，并生成更精准的目标边界框。本研究展示了联邦学习在制造业质量检测任务中的潜力，同时为利用YOLOv5与FedAvg开展联邦目标检测的性能与可行性提供了重要见解。