Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
This addresses the need for secure and fair collaborative model training among organizations, particularly in sensitive domains like healthcare, though it appears incremental as it builds on existing encryption and blockchain techniques.
The paper tackles the problem of ensuring data privacy, trustworthy iterative learning, and equitable model sharing in distributed learning across a coalition of organizations, achieving this through secure architectures that combine encryption and blockchain mechanisms, as demonstrated in a deep learning convolutional neural network trained on medical images.
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.