Federated learning enhanced by differential privacy has emerged as a popular approach to better safeguard the privacy of client-side data by protecting clients' contributions during the training process. Existing solutions typically assume a uniform privacy budget for all records and provide one-size-fits-all solutions that may not be adequate to meet each record's privacy requirement. In this paper, we explore the uncharted territory of cross-silo FL with record-level personalized differential privacy. We devise a novel framework named rPDP-FL, employing a two-stage hybrid sampling scheme with both client-level sampling and non-uniform record-level sampling to accommodate varying privacy requirements. A critical and non-trivial problem is to select the ideal per-record sampling probability q given the personalized privacy budget {\epsilon}. We introduce a versatile solution named Simulation-CurveFitting, allowing us to uncover a significant insight into the nonlinear correlation between q and {\epsilon} and derive an elegant mathematical model to tackle the problem. Our evaluation demonstrates that our solution can provide significant performance gains over the baselines that do not consider personalized privacy preservation.

我们探索了记录级个性化差分隐私的跨域FL的未知领域，提出了一个名为rPDP-FL的新框架，采用两阶段混合抽样方案，通过客户端级别和非均匀记录级别抽样来满足不同的隐私需求。我们介绍了一个名为Simulation-CurveFitting的多功能解决方案，通过揭示q和ε之间的非线性相关性并推导出一个优雅的数学模型来解决问题。我们的评估表明，相比不考虑个性化隐私保护的基线，我们的解决方案可以提供显著的性能提升。

跨领域的联邦学习与基于记录的个性化差分隐私