Failure Prediction in Production Line Based on Federated Learning: An Empirical Study

TL;DR

This empirical study demonstrates that federated learning can effectively replace centralized learning for failure prediction in manufacturing, preserving data privacy without sacrificing model performance across heterogeneous datasets.

Contribution

The paper introduces federated SVM and random forest algorithms tailored for manufacturing failure prediction and provides an experimental framework comparing FL and CL performance.

Findings

FL and CL have comparable performance on various test datasets.

Heterogeneous data enhances the effectiveness of FL.

FL can replace CL without performance loss in failure prediction.

Abstract

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.