Cross-silo Federated Learning with Record-level Personalized Differential Privacy

CoRR(2024)

引用 0|浏览10
暂无评分
摘要
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 ϵ. We introduce a versatile solution named Simulation-CurveFitting, allowing us to uncover a significant insight into the nonlinear correlation between q and ϵ 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.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要