Connecting Manufacturing Sector Data Ecosystems with Federated Learning

Jonas Kallisch () and Jorge Marx Gómez ()
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Jonas Kallisch: OFFIS Institute for Information Technology
Jorge Marx Gómez: Carl Von Ossietzky University Oldenburg

A chapter in Advancement in Embedded and Mobile Systems, 2026, pp 37-52 from Springer

Abstract: Abstract This paper explores the potential of combining Data Ecosystems in the manufacturing sector with Federated Learning, to benefit from intercompany data analytics, while maintaining sovereignty. The paper starts with an overview on the state-of-the-art of data sharing and Data Ecosystems in the manufacturing sector. The benefits, challenges, and the main reason for the current lack of intercompany data exchange and the use of Data Ecosystems are described. As the main barrier to join Data Ecosystems of the manufacturing companies seems to be the data sovereignty, the paper investigates into Federated Learning, as a solution to this issue. The research investigates existing Data Ecosystems to determine their readiness for integrating Federated Learning. Through literature reviews and practical experiments, the study finds that while many ecosystems recognize the potential of Federated Learning, none currently enables the use of Federated Learning inside the system. The paper proposes architectures for integrating Federated Learning into these ecosystems, highlighting the benefits and feasibility of such integration. The findings suggest that enabling Federated Learning in Data Ecosystems can enhance cross-company data analysis, leading to improved operational efficiency and innovation in the manufacturing sector. Further research and development are essential to realize these benefits.

Keywords: Federated learning; Data sharing; Data ecosystems; Data analytics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-99219-3_3

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DOI: 10.1007/978-3-031-99219-3_3

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