Towards AI: Federated Models in Life Sciences

Privacy has long been seen as a blocker in health and life sciences — a constraint on how fast we can collaborate, train models, or deploy solutions across borders and business units. But that view is changing.

We’re entering a new phase: one where privacy is not a blocker, but a design constraint — something to engineer around, not fear. At the center of this shift is federated learning and a broader movement toward compute interoperability: sending code and models to the data, rather than centralizing sensitive data in one place.

This shift is transforming how pharma, providers, data vendors, and research institutions collaborate.

Read more at Towards AI