Consensus-Regularized Federated Learning for Superior Generalization in Wind Turbine Diagnostics

Ensuring the reliable operation of wind turbines is critical for the global transition to sustainable energy, yet it is challenged by faults that are difficult to detect in real-time. Traditional diagnostics rely on centralized data, which raises significant privacy and scalability concerns. To address these limitations, this study introduces a Consensus-Regularized Federated Learning (CR-FL) framework. This framework mathematically formalizes and mitigates the problem of “client drift” caused by heterogeneous data from different turbines by augmenting the local training objective with a proximal regularization term. This forces models to learn generalizable fault features while preserving data privacy. To validate our framework, we implemented a lightweight neural network within a federated paradigm and benchmarked it against a powerful, centralized Light Gradient Boosting Machine (LightGBM) model using real-world SCADA data. The federated training process, through its inherent constraint on local updates, acts as a practical implementation of our consensus-regularization principle. Model performance was comprehensively evaluated using accuracy, precision, F1-score, and Area Under the ROC Curve (AUC) metrics. The results demonstrate that our federated approach not only preserves privacy but also achieves superior performance in key metrics, including AUC and precision. This confirms that the regularizing effect of the federated process enables the global model to generalize better across heterogeneous data distributions than its centralized counterpart. This study provides a practical, scalable, and methodologically superior solution for fault diagnosis in wind turbine systems, paving the way for more collaborative and secure infrastructure monitoring.