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

作者：Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng · 年份：2024 · DOI：10.1145/3658644.3670351 · 被引用次数：18 · 研究领域：Privacy-Preserving Technologies in Data、Cryptography and Data Security、Stochastic Gradient Optimization Techniques

Federated learning (FL) 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 namedrPDP-FL, employing a two-stage hybrid sampling scheme with both uniform client-level sampling and non-uniform record-level sampling to accommodate varying privacy requirements.