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, DP in deep learning typically adversely affects atypical (often informative) data samples, making it difficult to assess the usefulness of individual contributions. In this work we investigate how to leverage gradient information to identify training samples of interest in private training settings. We show that there exist techniques which are able to provide the clients with the tools for principled data selection even in strictest privacy settings.

研究了如何在严格保护隐私的情况下，利用梯度信息来选择有利于模型训练的数据，解决在协同训练深度学习模型中，难以区分出有用数据点的问题。

利用梯度衍生的度量对不同ially private训练中的数据选择和估值进行优化