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 …

abstract analysts arxiv budget cloud cloud service collaborative cs.cr cs.dc cs.lg data data analysts distributed fair federated learning machine machine learning pipelines privacy raw scheduling service service providers training type