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Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management

Lei Zhao, Lin Cai, Wu-Sheng Lu

TL;DR

The paper addresses the challenge of robust, scalable federated learning for financial risk management under data heterogeneity and tail risks. It introduces FRAL-CSE, which combines distortion risk measures that prioritize tail outcomes with a central acceleration mechanism that leverages a quadratic, second-order sensitivity approximation to guide global updates without repeated local evaluations. Key contributions include (i) risk-aware local objectives with tail emphasis via the $eta$-quantile threshold $F_{X_k, \bm{w}}^{-1}(\beta)$ and slack variables, (ii) a central Newton-like update using the aggregated sensitivity matrix $\bm{S}_t$, and (iii) extensive experiments on non-IID financial data showing faster convergence and robustness to distribution shifts and dynamic participation. This framework enables privacy-preserving, scalable collaboration among financial institutions while enhancing resilience to extreme market conditions through tail-risk emphasis and curvature-aware optimization.

Abstract

In decentralized financial systems, robust and efficient Federated Learning (FL) is promising to handle diverse client environments and ensure resilience to systemic risks. We propose Federated Risk-Aware Learning with Central Sensitivity Estimation (FRAL-CSE), an innovative FL framework designed to enhance scalability, stability, and robustness in collaborative financial decision-making. The framework's core innovation lies in a central acceleration mechanism, guided by a quadratic sensitivity-based approximation of global model dynamics. By leveraging local sensitivity information derived from robust risk measurements, FRAL-CSE performs a curvature-informed global update that efficiently incorporates second-order information without requiring repeated local re-evaluations, thereby enhancing training efficiency and improving optimization stability. Additionally, distortion risk measures are embedded into the training objectives to capture tail risks and ensure robustness against extreme scenarios. Extensive experiments validate the effectiveness of FRAL-CSE in accelerating convergence and improving resilience across heterogeneous datasets compared to state-of-the-art baselines.

Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management

TL;DR

The paper addresses the challenge of robust, scalable federated learning for financial risk management under data heterogeneity and tail risks. It introduces FRAL-CSE, which combines distortion risk measures that prioritize tail outcomes with a central acceleration mechanism that leverages a quadratic, second-order sensitivity approximation to guide global updates without repeated local evaluations. Key contributions include (i) risk-aware local objectives with tail emphasis via the -quantile threshold and slack variables, (ii) a central Newton-like update using the aggregated sensitivity matrix , and (iii) extensive experiments on non-IID financial data showing faster convergence and robustness to distribution shifts and dynamic participation. This framework enables privacy-preserving, scalable collaboration among financial institutions while enhancing resilience to extreme market conditions through tail-risk emphasis and curvature-aware optimization.

Abstract

In decentralized financial systems, robust and efficient Federated Learning (FL) is promising to handle diverse client environments and ensure resilience to systemic risks. We propose Federated Risk-Aware Learning with Central Sensitivity Estimation (FRAL-CSE), an innovative FL framework designed to enhance scalability, stability, and robustness in collaborative financial decision-making. The framework's core innovation lies in a central acceleration mechanism, guided by a quadratic sensitivity-based approximation of global model dynamics. By leveraging local sensitivity information derived from robust risk measurements, FRAL-CSE performs a curvature-informed global update that efficiently incorporates second-order information without requiring repeated local re-evaluations, thereby enhancing training efficiency and improving optimization stability. Additionally, distortion risk measures are embedded into the training objectives to capture tail risks and ensure robustness against extreme scenarios. Extensive experiments validate the effectiveness of FRAL-CSE in accelerating convergence and improving resilience across heterogeneous datasets compared to state-of-the-art baselines.

Paper Structure

This paper contains 19 sections, 30 equations, 22 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Risk-Aware Optimization in Federated Learning
  4. The Distortion Risk Measure in Decision Making
  5. Robust Objective Formulation for Federated Optimization
  6. Sensitivity-Based Global Optimization for Robust Central Acceleration
  7. Risk-Aware Local Sensitivity Evaluation
  8. Central Acceleration via Sensitivity-Guided Optimization
  9. Experiments
  10. Experimental Setup
  11. Model Accuracy and Scalability
  12. Robustness to Evolving Market Conditions
  13. Robustness in Dynamic Participation and Client Dropouts
  14. Conclusion
  15. Additional Experiments
  16. ...and 4 more sections

Figures (22)

  • Figure 1: Impact of increasing FL scales on test accuracy with full client participation and no dropout.
  • Figure 2: Impact of increasing FL scales on train loss with full client participation and no dropout.
  • Figure 3: Impact of different train-test splits on test accuracy with $50$ clients.
  • Figure 4: Impact of different participation rates on test accuracy with 10 clients.
  • Figure 5: Impact of dynamic client dropout on test accuracy with $10$ clients.
  • ...and 17 more figures