  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.
