针对风机叶片结冰检测中的数据异构与类别不平衡问题，提出原型联邦学习方法。
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
- 基于原型的联邦学习框架，适应不同环境下的数据异构性。
- 在真实20台风机数据上，mFβ提升19.64%，mBA提升5.73%。
- 适合工业界部署于隐私敏感、样本不均衡的风力发电监测场景。
风电场通常位于高纬度地区，面临叶片结冰的高风险。传统集中式训练方法引发严重隐私担忧。为增强风机叶片结冰检测中的数据隐私保护，采用联邦学习（FL）。然而，因不同风电场环境差异导致的数据异构性，影响模型优化能力；同时，风机数据存在类别不平衡，使模型倾向于识别多数类，忽略关键结冰异常。为此，我们提出一种面向异构环境与类别不平衡数据的联邦原型学习模型以检测风机叶片结冰，并设计了一种对比监督损失函数应对类别不平衡问题。在来自两个风电场共20台风机的真实数据上进行实验，结果表明，该方法优于五种联邦学习模型和五种类别不平衡处理方法，在mFβ上平均提升19.64%，在mBA上提升5.73%，相较第二优方法BiFL显著更优。
原文摘要 · Abstract (English)
Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in detecting wind turbine blade icing, traditional federated learning (FL) is employed. However, data heterogeneity, resulting from collections across wind farms in varying environmental conditions, impacts the model's optimization capabilities. Moreover, imbalances in wind turbine data lead to models that tend to favor recognizing majority classes, thus neglecting critical icing anomalies. To tackle these challenges, we propose a federated prototype learning model for class-imbalanced data in heterogeneous environments to detect wind turbine blade icing. We also propose a contrastive supervised loss function to address the class imbalance problem. Experiments on real data from 20 turbines across two wind farms show our method outperforms five FL models and five class imbalance methods, with an average improvement of 19.64\% in \( mF_β \) and 5.73\% in \( m \)BA compared to the second-best method, BiFL.
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