为金融医疗等强监管领域设计抗故障的隐私保护联邦学习框架
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries
- 客户端加本地差分隐私，结合时间状态容错与哈希验证机制
- 支持训练连续性与精确状态恢复，通过零知识证明保障模型完整性
- 适合需要审计、合规且数据敏感的企业级AI部署
联邦学习（FL）已成为金融、医疗等受监管领域实现隐私保护机器学习的关键范式。然而，传统FL策略在容错能力、应对客户端与服务器并发故障的系统韧性，以及提供可验证的强隐私保障方面存在显著挑战，可能导致训练中断、数据丢失、模型被篡改或违反数据保护法规（如GDPR、CCPA）。本文提出差分隐私鲁棒时序联邦学习（DP-RTFL）框架，旨在确保训练连续性、精确状态恢复和强数据隐私。该框架在客户端集成本地差分隐私（LDP），并结合鲁棒的时间状态管理与完整性验证机制（如基于哈希的承诺，文中称为零知识完整性证明，即ZKIPs）。该框架特别适用于信用风险评估等涉及敏感金融数据的关键应用，具备操作鲁棒性、可审计性与企业级可扩展性。代码已开源。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruptions, data loss, compromised model integrity, and non-compliance with data protection regulations (e.g., GDPR, CCPA). This paper introduces Differentially Private Resilient Temporal Federated Learning (DP-RTFL), an advanced FL framework designed to ensure training continuity, precise state recovery, and strong data privacy. DP-RTFL integrates local Differential Privacy (LDP) at the client level with resilient temporal state management and integrity verification mechanisms, such as hash-based commitments (referred to as Zero-Knowledge Integrity Proofs or ZKIPs in this context). The framework is particularly suited for critical applications like credit risk assessment using sensitive financial data, aiming to be operationally robust, auditable, and scalable for enterprise AI deployments. The implementation of the DP-RTFL framework is available as open-source.
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。