Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there is very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine and federated random forest algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an experiment process for evaluating the effectiveness between the FL and CL algorithms; (3) finds that the performance of FL and CL are not significantly different on the global testing data, on the random partial testing data, and on the estimated unknown Bosch data, respectively. The fact that the testing data is heterogeneous enhances our findings. Our study reveals that FL can replace CL for failure prediction.

跨组织的数据保护限制了集中式学习（CL）技术的应用。联合学习（FL）使多个参与者可以建立学习模型而无需共享数据。但是，关于FL在智能制造中的研究很少。本文介绍了基于FL的生产线故障预测的实证研究结果。本文（1）分别针对水平FL和垂直FL场景设计了联合支持向量机和联合随机森林算法；（2）提出了评估FL算法和CL算法之间有效性的实验过程；（3）发现FL和CL的性能在全局测试数据，随机局部测试数据和估计的未知Bosch数据上分别没有显着差异。测试数据是异构的这一事实增强了我们的发现。我们的研究表明，FL可以代替CL进行故障预测。