Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.

翻译：数据驱动机器学习在工业4.0的推进中发挥着关键作用，特别是在增强预测性维护与质量检测领域。联邦学习使多方参与者能够在不泄露数据隐私与保密性的前提下协同开发机器学习模型。本文评估了不同联邦学习聚合方法的性能，并将其与集中式训练和本地训练方法进行了比较。我们的研究基于四个具有不同数据分布的数据集展开。结果表明，联邦学习的性能高度依赖于数据本身及其在客户端之间的分布情况。在某些场景下，联邦学习可成为传统集中式或本地训练方法的有效替代方案。此外，我们引入了一个来自真实质量检测场景的新型联邦学习数据集。