Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management
This addresses robust federated learning for financial risk management, offering incremental improvements in scalability and stability.
They tackled the problem of robust and efficient federated learning in decentralized financial systems by proposing FRAL-CSE, which uses a central acceleration mechanism with sensitivity estimation and distortion risk measures, resulting in accelerated convergence and improved resilience across heterogeneous datasets.
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.