Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training

arXiv:2305.02942v3 Announce Type: replace-cross
Abstract: Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to participate. The first issue can be addressed through the combination of distributed machine learning techniques (e.g. federated learning) and privacy enhancing technologies (PET), such as the differentially private (DP) model training. The second challenge can be addressed by rewarding the participants for giving access to data …

arxiv can cs.ai cs.cr cs.lg data data owner distributed federation high incentives issue machine machine learning machine learning models metrics private quality regulatory task training valuation