Financial Data Analysis with Robust Federated Logistic Regression (2504.20250v1)

Abstract: 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.

Financial Data Analysis with Robust Federated Logistic Regression

The paper, "Financial Data Analysis with Robust Federated Logistic Regression," presents an innovative federated learning framework tailored to the specific needs and challenges of financial data analysis. The authors aim to develop a method that not only ensures data privacy but also enhances the interpretability of models while maintaining robustness against outliers.

The primary contribution of this work is the introduction of a Federated Logistic Regression (FLR) framework that addresses the constraints of having distributed financial data across multiple sites. This federated setting prevents raw data from leaving local devices, thus safeguarding user privacy. The proposed method crucially emphasizes the trade-offs among three key aspects: data privacy, model interpretability, and robustness to data anomalies.

Methodology

The framework relies on federated learning, a paradigm that allows training machine learning models using data situated at different locations without necessitating data centralization. In this paper, logistic regression serves as the basis for model training due to its inherent interpretability and simplicity. The FLR framework stands out by incorporating robust aggregation methods at the server side, which include coordinate-wise median and trimmed mean strategies, to mitigate the impact of outliers that might skew model performance in traditional mean-based federated learning setups.

The authors meticulously validate their approach on a variety of public datasets, performing extensive experiments with both IID and non-IID data distributions. Two primary scenarios are explored: one where data is evenly distributed across nodes (IID) and another that mimics real-world non-IID distributions. This setup challenges the federated model with data imbalance issues typically observed in financial applications.

Results

The results of the paper indicate that the proposed FLR framework achieves performance comparable to centralized models like traditional Logistic Regression, Decision Trees, and K-Nearest Neighbors, particularly from a robustness perspective in the presence of outliers. Specifically, FLR with robust aggregation methods exhibits remarkable resilience to performance degradation when faced with adversarial clients simulating outliers—an intrinsic risk in decentralized data environments.

A stark observation from the findings is the enhanced performance of robust aggregation methods over standard mean-based approaches, especially as the percentage of outliers increases. These methods demonstrate minimal performance drops, substantiating their effectiveness in maintaining model integrity against data corruption or adversarial attacks.

Implications and Future Directions

This paper has significant implications for real-world financial applications where data privacy, anomaly resistance, and interpretability are crucial. Federated learning frameworks, as demonstrated by this research, provide a viable pathway to leverage distributed data analytics without compromising sensitive client data. The interpretability aspect of logistic regression ensures transparency in decision-making processes, a vital characteristic for applications like credit scoring and fraud detection.

Future investigations can delve into more sophisticated aggregation strategies capable of handling increasingly complex adversarial attacks while minimizing communication overhead—a critical consideration for federated learning. Additionally, exploring the integration of more intricate models, such as neural networks, within the federated learning setting might reveal interesting trade-offs between accuracy, interpretability, and computational efficiency.

In conclusion, this paper introduces a robust, privacy-preserving federated learning strategy tailored for financial data analysis, offering a compelling alternative to traditional centralized methods, particularly in scenarios abundant with data anomalies. As the adoption of federated learning grows, such frameworks will likely play an indispensable role in the evolution of secure and transparent financial data analytics.