提出联邦学习下的自动特征工程方法，实现跨客户端高效建模。
Federated Automated Feature Engineering
- 针对水平、垂直和混合联邦场景设计新型自动特征工程算法
- 联邦环境下模型测试性能接近中心化处理的基准水平
- 适合数据隐私要求高、无法集中数据的工业应用
自动特征工程（AutoFE）可自动从原始特征生成新特征，以提升预测性能，且无需大量人工干预或领域知识。尽管已有多种AutoFE算法，但在联邦学习（FL）场景中，数据分布在多个客户端且不共享，相关研究极为有限。本文首次为水平、垂直和混合联邦学习设置提出了AutoFE算法，三者区别在于数据在客户端间的分布方式。据我们所知，这是首个针对水平和混合联邦场景的AutoFE方案。实验表明，联邦环境下的AutoFE模型在下游测试上的表现接近于数据集中处理时的性能基准。
原文摘要 · Abstract (English)
Automated feature engineering (AutoFE) is used to automatically create new features from original features to improve predictive performance without needing significant human intervention and domain expertise. Many algorithms exist for AutoFE, but very few approaches exist for the federated learning (FL) setting where data is gathered across many clients and is not shared between clients or a central server. We introduce AutoFE algorithms for the horizontal, vertical, and hybrid FL settings, which differ in how the data is gathered across clients. To the best of our knowledge, we are the first to develop AutoFE algorithms for the horizontal and hybrid FL cases, and we show that the downstream test scores of our federated AutoFE algorithms is close in performance to the case where data is held centrally and AutoFE is performed centrally.
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