Abstract
Intelligent production requires maximum downtime avoidance since downtimes lead to economic loss. Thus, Industry 4.0 (today’s IoT-driven industrial revolution) is aimed at automated production with real-time decision-making and maximal uptime. To achieve this, new technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Autonomous Guided Vehicles (AGVs) are integrated into production to optimize and automate many production processes. The increasing use of AGVs in production has far-reaching consequences for industrial communication systems. To make AGVs in production even more effective, we propose to use Federated Learning (FL) which provides a secure exchange of experience between intelligent manufacturing devices to improve prediction accuracy. We conducted research in which we exchanged experiences between the three virtual devices, and the results confirm the effectiveness of this approach in production environments.
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Acknowledgements
The research was supported by the Norway Grants 2014–2021 operated by the National Centre for Research and Development under the project “Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective” (Project Contract no.: NOR/POL-NOR/CoBotAGV /0027/2019-00), the Polish Ministry of Science and Higher Education as a part of the CyPhiS program at the Silesian University of Technology, Gliwice, Poland (Contract No.POWR.03.02.00-00-I007/17-00), and by Statutory Research funds of the Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland (grants no. 02/100/BKM21/0015, 02/100/BKM22/0020 and 02/100/BK_22/0017).
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Shubyn, B. et al. (2022). Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_35
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