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