[maximeago]

No, we do not. Pysyft was mostly first designed to do federated learning. Sarus targets organizations that have their data in one central repository in a trusted curator model. It lets external data practitioners query that data with all sorts of data jobs (not just ML, but also SQL analysis, and spark soon).

[nerdyadventurer]

side question: Is federated learning used in production? if not why?

[maximeago]

No, we don't do federated learning at Sarus today. We operate in the trusted curator model: a party has a centralized database and lets external practitioner leverage it. This is the most common setup in the industry (think hospitals, health insurance companies, banks, streaming services...).

That being said, Sarus can be used to protect one node of a federated learning network. For instance each hospital could have a Sarus instance. The data scientist would need to take care of the orchestration of the nodes themselves but the Sarus API would make their life easy to interact with each data source, especially if all the sources are not identical.