{"id":676479,"date":"2025-06-21T06:40:31","date_gmt":"2025-06-21T03:40:31","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/machine-learning-model-predicts-heat-resistant-steel-durability-while-preserving-data-confidentiality\/"},"modified":"2025-06-21T06:40:31","modified_gmt":"2025-06-21T03:40:31","slug":"machine-learning-model-predicts-heat-resistant-steel-durability-while-preserving-data-confidentiality","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/machine-learning-model-predicts-heat-resistant-steel-durability-while-preserving-data-confidentiality\/","title":{"rendered":"Machine learning model predicts heat-resistant steel durability while preserving data confidentiality"},"content":{"rendered":"
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\n Distributed learning conducted by each organization enabled the integration of model parameters without compromising data confidentiality, leading to improved accuracy in the lifetime prediction of heat-resistant materials. Credit: Masahiko Demura, National Institute for Materials Science<\/a>
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NIMS and its collaborators have developed a model designed to predict the long-term durability of a range of heat-resistant steel materials by performing machine learning while preserving the confidentiality of each organization’s data. This research is published<\/a> in Tetsu-to-Hagan\u00e9<\/i>.<\/p>\n

Private companies’ proprietary materials data is highly confidential, making cross-organizational sharing of it for collaborative R&D challenging. However, generating such data is extremely time-consuming and costly, so cross-organizational data collaboration is desirable. In particular, it can take more than a decade to acquire lifetime data for heat-resistant materials used in power generation facilities, highlighting the need for industry\u2013public sector collaboration.<\/p>\n

NIMS developed a system enabling multiple organizations (six private companies and two national R&D institutes) to independently perform machine learning using their own local data while preserving its confidentiality (i.e., through federated learning).<\/p>\n

As a result, they jointly constructed a “global model” capable of predicting the long-term durability of heat-resistant steel materials. The global model demonstrated significantly higher predictive accuracy than a local model built solely using NIMS’ data. This represents the first example of industry\u2013public sector data collaboration through federated learning.<\/p>\n

These achievements are expected to promote industry\u2013public sector data collaboration across a broad range of materials research fields. The federated learning system developed by NIMS is publicly available and open source. Going forward, NIMS plans to act as a coordinator, fostering collaboration to meet growing demands for industry\u2013public sector partnerships.<\/p>\n

The federated learning system used in this study was developed and released as open source<\/a> by NIMS and Elix.<\/p>\n

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More information:<\/strong>
\n\t\t\t\t\t\t\t\t\t\t\t\tJunya Sakurai et al, Federated Learning of Creep Rupture Time and High Temperature Tensile Strength Prediction Models, Tetsu-to-Hagan\u00e9<\/i> (2025). DOI: 10.2355\/tetsutohagane.TETSU-2024-124<\/a><\/p>\n<\/div>\n

\n Provided by
\n National Institute for Materials Science
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\n Citation<\/strong>:
\n Machine learning model predicts heat-resistant steel durability while preserving data confidentiality (2025, June 20)
\n retrieved 20 June 2025
\n from https:\/\/techxplore.com\/news<\/a>\/2025-06-machine-resistant-steel-durability-confidentiality.html\n <\/p>\n

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