Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
This is an incremental study applying existing federated learning methods to a new domain (intelligent manufacturing) for failure prediction, addressing data protection issues across organizations.
The paper tackled failure prediction in manufacturing production lines using federated learning to address data privacy concerns, finding that federated learning performed comparably to centralized learning across various testing scenarios, including heterogeneous data.
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 are 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 (FedSVM) and Federated Random Forest (FedRF) 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.