Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data

N effective strategy to reduce carbon emissions is to replace traditional fossil fuels by developing clean renewable Traditional federated learning (FL) offers an effective solution energy sources. Among renewable energy sources, wind to data privacy disclosure issue in centralized data-driven energy stands out as one of the most significant, alongside methods. Under the FL framework, each turbine contributes hydropower [1]. Therefore, the efficient operation of wind its own data to jointly train a global model without direct turbines is crucial to maximize energy output. To optimize data exchange [10]. This collaborative learning method avoids the harnessing of wind energy, wind farms are commonly centralized data storage and protects the privacy and security established on ridges, mountaintops, or other elevated areas. of data. FL has already been first applied to detect blade icing The low-temperature climate in these areas can lead to blade in wind turbines using a heterogeneous framework [11].