Financial Data Analysis with Robust Federated Logistic Regression
This work addresses privacy-preserving and interpretable financial data analysis for distributed clients, but it is incremental as it builds on existing federated learning methods with robustness enhancements.
The study tackled financial data analysis in a federated setting by proposing a robust federated logistic regression framework that balances privacy, interpretability, and outlier robustness, achieving comparable performance to centralized algorithms like Logistic Regression, Decision Tree, and K-Nearest Neighbors in binary and multi-class classification tasks on IID and non-IID data.
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.