用联邦学习预测全美各州金融困境，保护隐私还可解释。
Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
- 分州联邦学习，不集中数据即可建模
- 识别全国与各州特有的金融风险因素
- 适合关注隐私合规与社会公平的金融研究者
我们首次将联邦学习（FL）应用于美国国家金融能力调查，提出一种可解释框架，用于预测全美50个州及华盛顿特区的消费者金融困境，无需集中敏感数据。采用跨孤岛联邦学习架构，将每个州视为独立数据孤岛，模拟全国金融系统的实际治理结构。与以往工作不同，本方法融合两种互补的可解释AI技术，识别出全球（全国）和本地（州级）的金融困难预测因子，如债务催收联系。针对高度分类、不平衡的调查数据，开发了专用机器学习模型。该研究为金融早期预警系统提供了可扩展、符合监管要求的范例，展示了联邦学习在消费者信用风险与金融包容性等社会责任型应用中的潜力。
原文摘要 · Abstract (English)
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setup treats each state as a distinct data silo, simulating real-world governance in nationwide financial systems. Unlike prior work, our approach integrates two complementary explainable AI techniques to identify both global (nationwide) and local (state-specific) predictors of financial hardship, such as contact from debt collection agencies. We develop a machine learning model specifically suited for highly categorical, imbalanced survey data. This work delivers a scalable, regulation-compliant blueprint for early warning systems in finance, demonstrating how FL can power socially responsible AI applications in consumer credit risk and financial inclusion.
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