Dopamine: Differentially Private Federated Learning on Medical Data

Abstract

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.

arXiv commentThe Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)

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• Federated Learning for Smart Healthcare: A Survey
• Byzantine-Robust and Privacy-Preserving Framework for FedML
• Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
• Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
• A Privacy-Preserving Federated Learning Approach for Kernel methods
• Efficient Hyperparameter Optimization for Differentially Private Deep Learning