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

• 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: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Deep Anomaly Detection; Federated Learning
• Online Access: https://hdl.handle.net/10356/159853
• Institution: Nanyang Technological University

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...