2025/04/28 by Kun Yang, Yang, Kun, Nikhil Krishnan +3
Computer Science · Decision Sciences · #Applications (stat.AP) #Data Quality and Management #FOS: Computer and information sciences #FOS: Economics and business #Federated learning #Field (mathematics) #Focus (optics) #General Finance (q-fin.GN) #Imbalanced Data Classification Techniques #Independent and identically distributed random variables #Logistic regression #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Raw data #Robustness (evolution) #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2504.20250
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.