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

arXiv:2305.02942v3 Announce Type: replace
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 …

abstract arxiv collaborative combination concerns cs.ai cs.cr cs.lg data decentralised distributed federation gradient incentives issue machine machine learning machine learning models metrics quality quality data regulatory through training type valuation