One Stone, Three Birds: Prototype-Enhanced Federated Learning for Mitigating Data Scarcity, Imbalance, and Heterogeneity in Blade Icing Detection Across Distributed Wind Farms

Wind turbine blade icing poses a critical challenge to wind power generation in high-latitude regions, necessitating innovative solutions for reliable icing detection. To address this challenge while leveraging the abundance of unlabeled data and preserving data privacy, this study proposes a novel federated semi-supervised prototype learning framework, FedIce. By integrating prototype learning and federated learning, FedIce extracts representative class prototypes at the client level and performs global model updates through federated averaging, significantly enhancing robustness against data heterogeneity. Additionally, it incorporates an advanced separation margin strategy to effectively alleviate the adverse effects of class imbalance. Comprehensive experiments using real-world datasets from 20 wind turbines across two wind farms demonstrate that FedIce outperforms existing methods, achieving a remarkable 95.58% improvement in the mF_{\beta } metric and a 33.25% enhancement in the mBA metric compared to FedMatch.