  The amount of biomedical data continues to grow rapidly. However, collecting
data from multiple sites for joint analysis remains challenging due to
security, privacy, and regulatory concerns. To overcome this challenge, we use
Federated Learning, which enables distributed training of neural network models
over multiple data sources without sharing data. Each site trains the neural
network over its private data for some time, then shares the neural network
parameters (i.e., weights, gradients) with a Federation Controller, which in
turn aggregates the local models, sends the resulting community model back to
each site, and the process repeats. Our Federated Learning architecture,
MetisFL, provides strong security and privacy. First, sample data never leaves
a site. Second, neural network parameters are encrypted before transmission and
the global neural model is computed under fully-homomorphic encryption.
Finally, we use information-theoretic methods to limit information leakage from
the neural model to prevent a curious site from performing model inversion or
membership attacks. We present a thorough evaluation of the performance of
secure, private federated learning in neuroimaging tasks, including for
predicting Alzheimer's disease and estimating BrainAGE from magnetic resonance
imaging (MRI) studies, in challenging, heterogeneous federated environments
where sites have different amounts of data and statistical distributions.
