Dopamine: Differentially Private Secure Federated Learning on Medical Data

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.