  Distributed learning across a coalition of organizations allows the members
of the coalition to train and share a model without sharing the data used to
optimize this model. In this paper, we propose new secure architectures that
guarantee preservation of data privacy, trustworthy sequence of iterative
learning and equitable sharing of the learned model among each member of the
coalition by using adequate encryption and blockchain mechanisms. We exemplify
its deployment in the case of the distributed optimization of a deep learning
convolutional neural network trained on medical images.
