With the rapid digital transformation of manufacturing, vast amounts of data are being generated and analyzed to uncover valuable patterns in areas such as energy efficiency, predictive maintenance, production scheduling etc. However, much of this data and the intelligence derived from it remain isolated within individual companies. This is strongly influenced by companies reluctance to share data due to concerns over privacy and security associated with the commercially sensitive information. As a result, the potential shared value that can be derived from a richer, larger pool of data and intelligence across multiple companies remains untapped. While solutions such as federated learning exist to address privacy and security issues, strong governance so that the privacy is preserved is crucial to its successful implementation. Currently, there is a lack of software infrastructure that guarantees data sovereignty and governance for data owners in this space. This paper introduces COllaboRative Data Space (CORDS), a framework that enables companies to engage in a machine learning model-sharing ecosystem, providing full control over the access and usage of their data. Aligned with the European Data Space initiative, CORDS aims to foster trusted collaboration by providing a software infrastructure constituting a set of tools for both intra and inter-organization data asset management and ML model exchange. To the best of our knowledge, CORDS is the first minimum viable data space (MVDS) designed to address the broader challenges of sovereignty, interoperability, compliance & governance in cross-party ML model sharing. This paper also highlights the value of data sharing by applying CORDS to a use-case focused on improving energy efficiency in manufacturing. Extensive performance evaluation showcases CORDS’ utility in securely managing data assets and facilitating machine learning model exchanges. CORDS is available as open-source software, supporting further research and practical applications of trusted data spaces in both academia and industry.

中文翻译：

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在制造数据空间中实现安全且自主的机器学习模型交换


随着制造业的快速数字化转型，正在生成和分析大量数据，以发现能源效率、预测性维护、生产调度等领域的宝贵模式。然而，这些数据和从中获得的大部分情报仍然孤立在各个公司内部。由于担心与商业敏感信息相关的隐私和安全，公司不愿共享数据，这在很大程度上受到了影响。因此，从多家公司更丰富、更大的数据和情报池中可以获得的潜在共享价值仍未得到开发。虽然存在诸如联合学习之类的解决方案来解决隐私和安全问题，但强有力的治理以保护隐私对于其成功实施至关重要。目前，缺乏软件基础设施来保证该领域数据所有者的数据主权和治理。本白皮书介绍了 COllaboRative Data Space （CORDS），该框架使公司能够参与机器学习模型共享生态系统，从而完全控制其数据的访问和使用。CORDS 与欧洲数据空间倡议保持一致，旨在通过提供软件基础设施来促进可信协作，该基础设施构成了一组用于组织内部和组织间数据资产管理和 ML 模型交换的工具。据我们所知，CORDS是首个最小可行数据空间（MVDS），旨在解决跨党派ML模型共享中更广泛的主权、互操作性、合规性和治理挑战。本文还通过将 CORDS 应用于专注于提高制造能效的用例，强调了数据共享的价值。 广泛的性能评估展示了 CORDS 在安全管理数据资产和促进机器学习模型交换方面的效用。CORDS 作为开源软件提供，支持学术界和工业界对可信数据空间的进一步研究和实际应用。