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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
Researchers have successfully developed a machine learning model capable of predicting the long-term durability of various heat-resistant steel materials. The innovative approach allows for collaborative machine learning while ensuring the confidentiality of each participating organization’s data. the findings were collaboration highly desirable. The long lead times required to acquire lifetime data for heat-resistant materials, notably those used in power generation, underscore the importance of partnerships between industry and the public sector.
The team created a system that enables multiple organizations (six private companies and two national R&D institutes) to independently perform machine learning using their own local data, maintaining it’s confidentiality through federated learning.
This collaborative effort resulted in a “global model” capable of predicting the long-term durability of heat-resistant steel materials. The global model exhibited substantially improved predictive accuracy compared to a local model built using only one organization’s data. This marks a significant achievement in industry-public sector data collaboration using federated learning.
These advancements are expected to foster greater industry-public sector data collaboration across diverse materials research areas. The federated learning system is publicly available and open source,with plans to facilitate further collaboration to meet the growing demand for industry-public sector partnerships.
The federated learning system used in this study was released as blank”>open source.
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Machine learning model predicts heat-resistant steel durability while preserving data confidentiality (2025,June 20)
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