Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach

Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, whic...

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Bibliographic Details
Main Authors: Liu, Yi, Garg, Sahil, Nie, Jiangtian, Zhang, Yang, Xiong, Zehui, Kang, Jiawen, Hossain, M. Shamim
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Deep Anomaly Detection
Federated Learning
Online Access:https://hdl.handle.net/10356/159853

Internet

https://hdl.handle.net/10356/159853

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