Abstract
Federated learning is a distributed machine learning method that is well-suited for the Industrial Internet of Things (IIoT) as it enables the training of machine learning models on distributed datasets. One of the most important advantages of using Federated Learning for Automated Guided Vehicles (AGVs) is its capability to optimize resource consumption. AGVs are typically resource-constrained systems and must operate within tight power and computational limits. By using Federated Learning, AGVs can perform model training and updating on-board, which reduces the amount of data that needs to be transmitted. This paper presents experiments to assess the consumption of resources of the Jetson Nano edge IoT device while training the Federated Learning model, and compares it with referential machine learning approaches.
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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), by Statutory Research funds of the Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland (02/100/BK_23/0027), research funds for young scientists (grants no. PB and BS: BKM/RAu7/2023), and by the ReActive Too project that has received funding from the European Union’s Horizon 2020 Research, Innovation and Staff Exchange Programme under the Marie Skłodowska-Curie Action (Grant Agreement No 871163).
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Shubyn, B. et al. (2023). Resource Consumption of Federated Learning Approach Applied on Edge IoT Devices in the AGV Environment. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-031-36030-5_39
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