Scholarlink

Leveraging data in collaboration with other organizations requires resolving a paradox: Organizations of Manufacturing Networks must find a way to share data while protecting their own intellectual property. Vertical Federated Learning (VFL) allows collaborative machine learning without compromising data privacy, as it keeps data local. It facilitates effective, secure data-driven enhancements across organizations and country borders, enabling more efficient, more sustainable, and more customer-focused production.

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Preserving Digital Sovereignty in Data-Driven Manufacturing Networks

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Anne Mareike Schlinkert,
  • Leonhard Kunczik,
  • Orlando Hohmeier,
  • Michael Kuehne-Schlinkert

摘要

Leveraging data in collaboration with other organizations requires resolving a paradox: Organizations of Manufacturing Networks must find a way to share data while protecting their own intellectual property. Vertical Federated Learning (VFL) allows collaborative machine learning without compromising data privacy, as it keeps data local. It facilitates effective, secure data-driven enhancements across organizations and country borders, enabling more efficient, more sustainable, and more customer-focused production.