DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service

arXiv:2402.09715v1 Announce Type: cross
Abstract: Federated learning (FL) has emerged as a prevalent distributed machine learning scheme that enables collaborative model training without aggregating raw data. Cloud service providers further embrace Federated Learning as a Service (FLaaS), allowing data analysts to execute their FL training pipelines over differentially-protected data. Due to the intrinsic properties of differential privacy, the enforced privacy level on data blocks can be viewed as a privacy budget that requires careful scheduling to cater to diverse training …

analysts arxiv budget cloud cloud service cloud service providers cs.cr cs.dc cs.lg data data analysts distributed fair federated federated learning machine machine learning model training pipelines prevalent privacy service service providers training