定制化数据就绪检测框架，保障隐私联邦学习的数据质量。
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
- 用户自定义数据就绪指标、规则与修复方案
- 在6个数据集上解决7类数据问题，提升模型性能
- 适合关注隐私保护下数据质量的开发者与研究者
隐私保护联邦学习（PPFL）是一种去中心化的机器学习方法，多个客户端协作训练模型，无需交换原始数据即可保护数据隐私与安全。然而，由于数据访问受限，确保各客户端数据具备高质量且适合联邦学习仍具挑战。本文提出CADRE（Customizable Assurance of Data Readiness），一种新型框架，允许用户根据具体联邦学习任务自定义数据就绪（DR）指标、规则与修复策略。CADRE基于用户设定生成全面的DR报告，确保数据在不泄露隐私的前提下满足联邦学习要求。我们通过将CADRE集成到现有PPFL框架中进行验证，在六个数据集上处理了七种不同的数据就绪问题。实验结果表明，CADRE在数据质量、隐私与公平性等多个维度均展现出良好的适应性与有效性，显著提升了联邦学习模型的性能与可靠性，同时更高效利用了可用资源。
原文摘要 · Abstract (English)
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific FL tasks. CADRE generates comprehensive DR reports based on the user-defined metrics, rules, and remedies to ensure datasets are prepared for FL while preserving privacy. We demonstrate a practical application of CADRE by integrating it into an existing PPFL framework. We conducted experiments across six datasets and addressed seven different DR issues. The results illustrate the versatility and effectiveness of CADRE in ensuring DR across various dimensions, including data quality, privacy, and fairness. This approach enhances the performance and reliability of FL models as well as utilizes valuable resources.
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