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

2021/01/25 by Ning Ge, Ge, Ning, Guanghao Li +5
Computer Science · #Blockchain Technology Applications and Security #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2101.11715

openalex publication_date 2021/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Berkeley View of Systems Challenges for AI
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
• Variational Federated Multi-Task Learning
• Privacy-preserving Federated Brain Tumour Segmentation
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
• Advances and Open Problems in Federated Learning

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