Privacy Preserving Federated Learning
Substra Framework Joins LF AI & Data as New Incubation Project!
For more information, read the press release.
Secure, traceable, distributed ML orchestration
Different privacy-enhancing technologies are being developed by the privacy community and tested by a growing number of interested parties. They contribute to advancing the options for reinforcing the privacy of datasets and models in data science projects, and are becoming increasingly instrumental.
Substra framework is a low-layer tool, offering secure, traceable, distributed orchestration of machine learning tasks among partners. It aims at being compatible with privacy-enhancing technologies to complement their use to provide efficient and transparent privacy-preserving workflows for data science. Its ambition is to make new scientific and economic data science collaborations possible.
Data locality
Data remain in their owner's data stores and are never transferred. AI models travel from one dataset to another.
Traceability
An immutable audit trail registers all the operations realized on the platform, simplifying certification of models.
Decentralized trust
All operations are orchestrated by a distributed ledger technology. There is no need for a single trusted actor or third party: security arises from the network.
Modularity
Substra framework is highly flexible: various permission regimes and workflow structures can be enforced corresponding to every specific use case.
