While rich medical datasets are hosted in hospitals distributed across countries, concerns on patients' privacy is a barrier against utilizing such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed medical data, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure multi-party aggregation, can establish a better privacy-utility trade-off than the existing approaches. Results on a diabetic retinopathy (DR) task show that Dopamine provides a privacy guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel differential privacy where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health.

翻译：虽然分布在不同国家的医院拥有丰富的医疗数据集,但对病人隐私的关切是妨碍利用这些数据来培训深入神经网络进行医疗诊断的障碍,我们提议多帕胺,这是在分布的医疗数据方面培训DPANIN的系统,该系统使用有差别-私人随机梯度的联邦学习(FL),结合安全的多党汇总,可以建立比现有方法更好的隐私-通用权衡。糖尿病复方疗法(DR)任务结果显示,多帕胺提供了靠近集中培训对应方的隐私保障,同时实现了比FL更好的分类准确性,同时在没有协调的情况下使用DPSGD的平行差异隐私。《守则》可在https://github.com/ipc-lab/puty-ml-for-health查阅。