Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162980
Title: Privacy-preserving anomaly detection in cloud manufacturing via federated transformer
Authors: Ma, Shiyao
Nie, Jiangtian
Kang, Jiawen
Lyu, Lingjuan
Liu, Ryan Wen
Zhao, Ruihui
Liu, Ziyao
Niyato, Dusit
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Ma, S., Nie, J., Kang, J., Lyu, L., Liu, R. W., Zhao, R., Liu, Z. & Niyato, D. (2022). Privacy-preserving anomaly detection in cloud manufacturing via federated transformer. IEEE Transactions On Industrial Informatics, 18(12), 8977-8987. https://dx.doi.org/10.1109/TII.2022.3167478
Project: AISG2-RP-2020-019
RG16/20
Journal: IEEE Transactions on Industrial Informatics
Abstract: With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the abovementioned severe challenges, this article customizes a weakly supervised edge computing anomaly detection framework, i.e., federated learning-based transformer framework (FedAnomaly), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and transformer. Furthermore, extensive case studies on four benchmark datasets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and transformer to deal with anomaly detection problems in cloud manufacturing.
URI: https://hdl.handle.net/10356/162980
ISSN: 1551-3203
DOI: 10.1109/TII.2022.3167478
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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