用敏感度估计提升金融联邦学习的稳定性与抗风险能力
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management
- 通过全局敏感度估算实现二阶优化，无需重复本地计算
- 融合尾部风险度量，提升极端场景下的系统韧性
- 适合金融风控、分布式模型训练等高可靠性场景
在去中心化金融系统中，稳健高效的联邦学习（FL）有望应对多样的客户端环境并保障系统性风险下的韧性。本文提出联邦风险感知学习与中心敏感度估计框架（FRAL-CSE），旨在增强协同金融决策中的可扩展性、稳定性和鲁棒性。其核心创新在于基于二次敏感度近似的全局模型动态逼近，结合本地稳健风险度量获取的敏感度信息，实现曲率感知的全局更新，高效融入二阶信息且无需反复本地重评估，显著提升训练效率与优化稳定性。此外，将扭曲风险度量嵌入训练目标，以捕捉尾部风险，确保对极端情景的鲁棒性。大量实验验证了该方法在异构数据集上加速收敛并提升韧性，优于当前先进基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。