English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the rapid development of cloud manufacturing, industrial production withedge computing as the core architecture has been greatly developed. However,edge devices often suffer from abnormalities and failures in industrialproduction. Therefore, detecting these abnormal situations timely andaccurately is crucial for cloud manufacturing. As such, a straightforwardsolution is that the edge device uploads the data to the cloud for anomalydetection. However, Industry 4.0 puts forward higher requirements for dataprivacy and security so that it is unrealistic to upload data from edge devicesdirectly to the cloud. Considering the above-mentioned severe challenges, thispaper customizes a weakly-supervised edge computing anomaly detectionframework, i.e., Federated Learning-based Transformer framework(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloudmanufacturing. Specifically, we introduce federated learning (FL) frameworkthat allows edge devices to train an anomaly detection model in collaborationwith the cloud without compromising privacy. To boost the privacy performanceof the framework, we add differential privacy noise to the uploaded features.To further improve the ability of edge devices to extract abnormal features, weuse the Transformer to extract the feature representation of abnormal data. Inthis context, we design a novel collaborative learning protocol to promoteefficient collaboration between FL and Transformer. Furthermore, extensive casestudies on four benchmark data sets verify the effectiveness of the proposedframework. To the best of our knowledge, this is the first time integrating FLand Transformer to deal with anomaly detection problems in cloud manufacturing.

Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer