为联邦学习预训练数据设计隐私保护定价机制，提升模型精度与效率
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
- 采用两阶段拍卖机制结合安全协议，实现无训练过程信息的预训练数据定价
- 客户端选择使后续联邦学习准确率提升超10%，比现有方法更优
- 相比训练中定价基线，精度提高2%以上，运行时间缩短3倍，适合高隐私场景
联邦学习（FL）作为主流的隐私保护机器学习范式，在医疗、金融等隐私敏感领域具有广阔应用前景。尽管学术界和产业界已投入大量精力优化基础联邦学习框架，但对数据定价机制的研究仍较为不足。与直接在训练前后进行定价不同，本文研究了更复杂且困难的预训练阶段定价问题——即在缺乏学习过程直接信息的情况下进行定价。为此，我们提出FLMarket，其结合两阶段拍卖机制与安全协议，有效解决效用与隐私之间的矛盾。通过全面实验验证，基于FLMarket的客户端选择策略可使后续联邦学习训练准确率提升超过10%，优于当前最优方法；同时，相比训练中定价基线，准确率提升超过2%，运行时间减少3倍。
原文摘要 · Abstract (English)
Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.
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