Abstract
Integration of artificial intelligence (AI) in a work environment is the key factor of the industry 4.0 revolution. Vertical and horizontal integration among different parties become relevant but privacy could be the issues. Federated learning (FL) has been widely used as a decentralized mechanism to cope with privacy preserving problems. However, the initial setup for FL in the real-world application is difficult and requires a lot of human involvement in daily operation. In addition, project key performance indicator which is important to assess enterprise collaboration accomplishment among partners has been rarely addressed. In this work, we develop a horizontal FL framework and modular dashboard to enable collaborative training among different parties. The module is available for key performance monitoring operational view on both server and clients in three aspects; computer-related indicators, machine learning-related indicators, and manufacturing related indicators. Using the proposed framework, it can provide insight to the user regarding FL results.
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Acknowledgment
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program. (Project No. P0022316).
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Bermudez Pillado, E.P. et al. (2023). A Framework for Privacy-Preserved Collaborative Learning in Smart Factory Environment. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_42
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DOI: https://doi.org/10.1007/978-3-031-27199-1_42
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