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

arXiv:2401.16251v3 Announce Type: replace
Abstract: 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 …

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