Prediction of mechanical properties of hot-rolled strip steel based on federated learning

As a critical material in industry, hot-rolled strip steel has a direct impact on downstream manufacturing quality due to the accuracy of its mechanical property prediction. However, in practical applications, the predictive performance of models is difficult to improve due to insufficient data sample sizes. When considering collaborative modeling across multiple production lines or factories, dual challenges of data heterogeneity and privacy protection arise. To address these issues, this paper proposes a method for predicting the mechanical properties of hot-rolled strip based on federated learning. The method first achieves feature dimension alignment and enables multi-party collaborative prediction of tensile strength, yield strength, and elongation of hot-rolled strip under the premise of ensuring data privacy and security. Its results are compared with those of models trained on single production line data. Experimental results demonstrate that this method exhibits good performance in all mechanical property prediction tasks. Additionally, to further optimize the scheme, this paper introduces a federated dimension optimization method based on feature importance to collaboratively screen out key factors that significantly affect mechanical properties.