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Machine learning is promising, but it often needs to process vast amounts ofsensitive data which raises concerns about privacy. In this white-paper, weintroduce Substra, a distributed framework for privacy-preserving, traceableand collaborative Machine Learning. Substra gathers data providers andalgorithm designers into a network of nodes that can train models on demand butunder advanced permission regimes. To guarantee data privacy, Substraimplements distributed learning: the data never leave their nodes; onlyalgorithms, predictive models and non-sensitive metadata are exchanged on thenetwork. The computations are orchestrated by a Distributed Ledger Technologywhich guarantees traceability and authenticity of information without needingto trust a third party. Although originally developed for Healthcareapplications, Substra is not data, algorithm or programming language specific.It supports many types of computation plans including parallel computation plancommonly used in Federated Learning. With appropriate guidelines, it can bedeployed for numerous Machine Learning use-cases with data or algorithmproviders where trust is limited.

Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning