Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

Abstract

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition's members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.

Keywords

• Distributed learning
• blockchain
• convolutional neural network
• federated byzantine agreement