Scaling Neuroscience Research Using Federated Learning

@article{Stripelis2021ScalingNR,
  title={Scaling Neuroscience Research Using Federated Learning},
  author={Dimitris Stripelis and J. Ambite and Pradeep Lam and Paul M. Thompson},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2021},
  pages={1191-1195}
}
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning is a promising approach to learn a joint model over data silos. This architecture does not share any subject data across sites, only aggregated parameters, often in encrypted environments, thus… 
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