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Journal of Intelligent Manufacturing

Erschienen in:

05.05.2023

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

verfasst von: Wei Guo, Yijin Wang, Xin Chen, Pingyu Jiang

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2024

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Abstract

Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gather the data to train a personalized model without compromising data privacy. To address this issue, we proposed a Federated Transfer Learning framework based on Auxiliary Classifier Generative Adversarial Networks named ACGAN-FTL. In the framework, Federated Learning (FL) trains a global model on decentralized datasets of the clients with data privacy-preservation and Transfer Learning (TL) transfers the knowledge from the global model to a personalized model with a relatively small data volume. ACGAN acts as a data bridge to connect FL and TL by generating similar probability distribution data of clients since the client datasets in FL cannot be directly used in TL for data privacy-preservation. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the performance of the proposed framework. The results show that ACGAN-FTL can not only obtain acceptable performance on 0.81 accuracy, 0.86 precision, 0.74 recall, and 0.79 F1 but also ensure data privacy-preservation in the whole learning process. Compared to the baseline method without FL and TL, the former metrics have increased by 13%, 11%, 16%, and 15% respectively. The experiments verify that the performance of the proposed ACGAN-FTL framework fulfills the requirements of industrial scenarios.

Vorheriger Artikel Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing

Nächster Artikel Multidomain variance-learnable prototypical network for few-shot diagnosis of novel faults

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Titel: Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application
verfasst von: Wei Guo
Yijin Wang
Xin Chen
Pingyu Jiang
Publikationsdatum: 05.05.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2024
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02126-z

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