Machine learning model predicts heat-resistant steel durability while preserving data confidentiality

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 in Tetsu-to-Hagané.

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–public sector collaboration.

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).

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–public sector data collaboration through federated learning.

These achievements are expected to promote industry–public 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–public sector partnerships.

The federated learning system used in this study was developed and released as open source by NIMS and Elix.