标题： 基于鲁棒联邦逻辑回归的金融数据分析 显示英文标题

标题： Financial Data Analysis with Robust Federated Logistic Regression

摘要： 在本研究中，我们专注于联邦设置下金融数据的分析，在这种设置中，数据分布在多个客户端或位置，并且原始数据永远不会离开本地设备。 我们的主要关注点不仅在于开发高效的联邦学习框架（以保护用户数据隐私），还在于设计更容易解释的模型的重要性。 此外，我们关心框架对异常值的鲁棒性。 为了实现这些目标，我们提出了一种基于稳健联邦逻辑回归的框架，力求在这几个目标之间取得平衡。 为了验证我们所提出的框架的可行性，我们不仅在独立同分布（IID）数据上，还在非独立同分布（non-IID）数据上，尤其是在涉及异常值的情景下，对其性能进行了仔细评估。 来自多个公共数据集的大量数值结果显示，我们所提出的方法在二元和多类分类任务中能够达到与经典集中式算法（如逻辑回归、决策树和K近邻算法）相当的性能。 显示英文摘要

摘要： In this study, we focus on the analysis of financial data in a federated setting, wherein data is distributed across multiple clients or locations, and the raw data never leaves the local devices. Our primary focus is not only on the development of efficient learning frameworks (for protecting user data privacy) in the field of federated learning but also on the importance of designing models that are easier to interpret. In addition, we care about the robustness of the framework to outliers. To achieve these goals, we propose a robust federated logistic regression-based framework that strives to strike a balance between these goals. To verify the feasibility of our proposed framework, we carefully evaluate its performance not only on independently identically distributed (IID) data but also on non-IID data, especially in scenarios involving outliers. Extensive numerical results collected from multiple public datasets demonstrate that our proposed method can achieve comparable performance to those of classical centralized algorithms, such as Logistical Regression, Decision Tree, and K-Nearest Neighbors, in both binary and multi-class classification tasks.