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| Study on Paper Moisture Anomaly Prediction Based on XGBoost and Federated Transfer Learning Integration |
| Received:September 06, 2025 Revised:October 09, 2025 |
| DOI：10.11980/j.issn.0254-508X.2026.03.021 |
| Key Words:water anomaly prediction federated learning transfer learning XGBoost domain adversarial neural network |
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| Abstract:In the intelligentization of the paper industry， accurate prediction of paper moisture content is crucial for quality control and energy efficiency optimization. Traditional methods suffer from limited model generalizability due to scarce and homogeneous single-enterprise samples， while also failing to meet data privacy protection requirements. To address these issues， this paper proposed a federated learning framework based on XGBoost， which ensured data privacy through a distributed architecture and incorporated a Domain-Adversarial Neural Network for cross-domain feature alignment to enhance generalization capability. Validated with data from two paper mills， the global model achieved an accuracy of 91.74%， with all performance metrics significantly outperforming local models and other comparative algorithms. The performance gap with centralized XGBoost was less than 1%. Although the two feature combinations constructed after feature alignment showed a slight performance decrease (≤1.5%)， they demonstrated cross-domain stability and collaborative benefits. |
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