• Corpus ID: 231728784

Dopamine: Differentially Private Federated Learning on Medical Data

@article{Malekzadeh2021DopamineDP,
  title={Dopamine: Differentially Private Federated Learning on Medical Data},
  author={M. Malekzadeh and Burak Hasircioglu and Nitish Mital and Kunal Katarya and Mehmet Emre Ozfatura and Deniz Gunduz},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.11693}
}
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… 

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