Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles

Bohdan Shubyn; Dariusz Mrozek; Taras Maksymyuk; Vaidy Sunderam; Daniel Kostrzewa; Piotr Grzesik; Paweł Benecki

doi:10.1007/978-3-031-08760-8_35

Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles

Bohdan Shubyn
, Dariusz Mrozek
, Taras Maksymyuk
, Vaidy Sunderam
, Daniel Kostrzewa
, Piotr Grzesik
, Paweł Benecki

Lviv Polytechnic National University
Emory University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

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.

Original language	English
Title of host publication	Computational Science - ICCS 2022, 22nd International Conference, Proceedings
Editors	Derek Groen, Clélia de Mulatier, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Maciej Paszynski, Jack J. Dongarra
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	409-421
Number of pages	13
ISBN (Print)	9783031087592
DOIs	https://doi.org/10.1007/978-3-031-08760-8_35
Publication status	Published - 2022
Event	22nd Annual International Conference on Computational Science, ICCS 2022 - London, United Kingdom Duration: 21 Jun 2022 → 23 Jun 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13353 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Annual International Conference on Computational Science, ICCS 2022
Country/Territory	United Kingdom
City	London
Period	21/06/22 → 23/06/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Artificial intelligence
Federated learning
Long-short term memory
Predictive maintenance
Recurrent neural networks
Smart production

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-08760-8_35

Cite this

Shubyn, B., Mrozek, D., Maksymyuk, T., Sunderam, V., Kostrzewa, D., Grzesik, P., & Benecki, P. (2022). Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. In D. Groen, C. de Mulatier, V. V. Krzhizhanovskaya, P. M. A. Sloot, M. Paszynski, & J. J. Dongarra (Eds.), Computational Science - ICCS 2022, 22nd International Conference, Proceedings (pp. 409-421). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13353 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08760-8_35

Shubyn, Bohdan ; Mrozek, Dariusz ; Maksymyuk, Taras et al. / Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. Computational Science - ICCS 2022, 22nd International Conference, Proceedings. editor / Derek Groen ; Clélia de Mulatier ; Valeria V. Krzhizhanovskaya ; Peter M.A. Sloot ; Maciej Paszynski ; Jack J. Dongarra. Springer Science and Business Media Deutschland GmbH, 2022. pp. 409-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Intelligent production requires maximum downtime avoidance since downtimes lead to economic loss. Thus, Industry 4.0 (today{\textquoteright}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.",

keywords = "Artificial intelligence, Federated learning, Long-short term memory, Predictive maintenance, Recurrent neural networks, Smart production",

author = "Bohdan Shubyn and Dariusz Mrozek and Taras Maksymyuk and Vaidy Sunderam and Daniel Kostrzewa and Piotr Grzesik and Pawe{\l} Benecki",

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Shubyn, B , Mrozek, D, Maksymyuk, T, Sunderam, V, Kostrzewa, D , Grzesik, P & Benecki, P 2022, Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. in D Groen, C de Mulatier, VV Krzhizhanovskaya, PMA Sloot, M Paszynski & JJ Dongarra (eds), Computational Science - ICCS 2022, 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13353 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 409-421, 22nd Annual International Conference on Computational Science, ICCS 2022, London, United Kingdom, 21/06/22. https://doi.org/10.1007/978-3-031-08760-8_35

Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. / Shubyn, Bohdan ; Mrozek, Dariusz; Maksymyuk, Taras et al.
Computational Science - ICCS 2022, 22nd International Conference, Proceedings. ed. / Derek Groen; Clélia de Mulatier; Valeria V. Krzhizhanovskaya; Peter M.A. Sloot; Maciej Paszynski; Jack J. Dongarra. Springer Science and Business Media Deutschland GmbH, 2022. p. 409-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13353 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles

AU - Shubyn, Bohdan

AU - Mrozek, Dariusz

AU - Maksymyuk, Taras

AU - Sunderam, Vaidy

AU - Kostrzewa, Daniel

AU - Grzesik, Piotr

AU - Benecki, Paweł

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Federated learning

KW - Long-short term memory

KW - Predictive maintenance

KW - Recurrent neural networks

KW - Smart production

UR - https://www.scopus.com/pages/publications/85134358983

U2 - 10.1007/978-3-031-08760-8_35

DO - 10.1007/978-3-031-08760-8_35

M3 - Conference contribution

AN - SCOPUS:85134358983

SN - 9783031087592

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 409

EP - 421

BT - Computational Science - ICCS 2022, 22nd International Conference, Proceedings

A2 - Groen, Derek

A2 - de Mulatier, Clélia

A2 - Krzhizhanovskaya, Valeria V.

A2 - Sloot, Peter M.A.

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A2 - Dongarra, Jack J.

PB - Springer Science and Business Media Deutschland GmbH

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ER -

Shubyn B , Mrozek D, Maksymyuk T, Sunderam V, Kostrzewa D , Grzesik P et al. Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles. In Groen D, de Mulatier C, Krzhizhanovskaya VV, Sloot PMA, Paszynski M, Dongarra JJ, editors, Computational Science - ICCS 2022, 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 409-421. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-08760-8_35