Secure Federated Learning for Neuroimaging. (arXiv:2205.05249v1 [cs.LG])

The amount of biomedical data continues to grow rapidly. However, the ability
to collect data from multiple sites for joint analysis remains challenging due
to security, privacy, and regulatory concerns. We present a Secure Federated
Learning architecture, MetisFL, which enables distributed training of neural
networks 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 …

arxiv federated learning learning neuroimaging