Cross-silo Federated Learning with Record-level Personalized Differential Privacy. (arXiv:2401.16251v2 [cs.CR] UPDATED)

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 …

arxiv budget client clients client-side data differential privacy federated federated learning may popular privacy process protecting record records safeguard size solutions training