A federated learning-driven data fusion strategy for the hardenability prediction of gear steel

Hardenability is a critical indicator for evaluating the mechanical performance and service reliability of gear steels. However, conventional Jominy end-quench testing is labor-intensive and time-consuming, and data sharing among different companies is often restricted, which further complicates hardenability assessment. To address these challenges, federated learning–driven data fusion strategy incorporating a multi-regularized attention residual network model for hardenability prediction (MRAN-J9) is proposed. In this strategy, collaborative models are trained on heterogeneous data from multiple sources, improving predictive accuracy while preserving the privacy of each participant’s raw data. Additionally, stable predictive performance is evaluated on a completely independent external validation dataset containing 755 samples (R² = 0.88, RMSE = 0.99 HRC), demonstrating the generalization capability and predictive stability. The results confirm the feasibility and effectiveness of federated learning for privacy-preserving multi-party collaborative modeling. Furthermore, integrating the MRAN-J9 model facilitates the effective exploitation of distributed multi-source data, providing a practical and reliable solution for hardenability prediction in complex industrial application scenarios.