Federated Learning for Advanced Manufacturing Based on Industrial IoT Data Analytics

• Published in: Implementing Industry 4. 0 Vol. 202; pp. 143 - 176
• Main Authors: Kanagavelu, Renuga, Li, Zengxiang, Samsudin, Juniarto, Hussain, Shaista, Yang, Feng, Yang, Yechao, Goh, Rick Siow Mong, Cheah, Mervyn
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Intelligent Systems Reference Library
• Subjects: Centralized learning; Federated learning; Industrial IoT smart manufacturing; Multi-party computation
• ISBN: 3030672697; 9783030672690
• ISSN: 1868-4394; 1868-4408
• DOI: 10.1007/978-3-030-67270-6_6

The recent surge in the volume of real-time condition-monitoring data generated from connected devices such as from Industrial Internet of Things (IIoT), has enabled wide spread adoption of machine learning techniques to improve the efficiency, effectiveness and intelligence of the industrial process. However, a key obstacle to this whole adoption is that the data is distributed across multiple industrial assets and are usually not shared across the industry partners as they are constrained by complicated administrative process, industry competition concerns and the need to comply to privacy laws. Federated Learning, a privacy preserving machine learning technique coined by Google, is a likely and efficient technique to overcome this obstacle. Federated Learning enables multiple industries to train the condition-monitoring data without revealing their corresponding data and assets. In this chapter, we present our proposed decentralized federated learning based solution and its integration with an IIoT smart manufacturing platform. We demonstrate the effectiveness of our platform with real world datasets and different machine learning algorithms. We observe that the proposed federated learning approach can achieved an equivalent performance compared to the traditional centralized-based learning approach in terms of accuracy and efficiency.