Leveraging gradient-derived metrics for data selection and valuation in differentially private training. (arXiv:2305.02942v1 [cs.LG])

Obtaining high-quality data for collaborative training of machine learning
models can be a challenging task due to A) the regulatory concerns and B) lack
of incentive to participate. The first issue can be addressed through the use
of privacy enhancing technologies (PET), one of the most frequently used one
being differentially private (DP) training. The second challenge can be
addressed by identifying which data points can be beneficial for model training
and rewarding data owners for sharing this data. However, …

data high issue machine machine learning machine learning models metrics privacy privacy enhancing technologies private quality regulatory task technologies training valuation