Federated Learning

The future of federated learning in healthcare starts here

A new decentralized machine learning technique to train machine learning models with multiple data providers.
Owkin OrbData Scientist computersData Scientist

Decentralized machine learning

Instead of gathering data on a single server, the data remains locked on servers as the algorithms and only the predictive models travel between the servers.

The goal of this approach is for each participant to benefit from a larger pool of data than their own, resulting in increased machine learning performance, while respecting data ownership and privacy.

Unlocking data at scale

The ability to train machine learning models at scale across multiple medical institutions without pooling data is a critical technology to help address patient privacy and data protection concerns.

A successful implementation of federated learning in healthcare could hold significant potential for enabling precision medicine at a large-scale; helping match the right treatment to the right patient at the right time.

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Transforming Healthcare with Federated Learning
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Discover how Owkin leverages federated learning to advance drug development and improve patient outcomes. By enabling secure collaboration between hospitals, research centers, and tech partners, Owkin can leverage patient data to build predictive models for personalized cancer treatments while prioritizing privacy.

Asset identification

Hospitals, research centers and biopharma companies need to start collaborating

Researchers can leverage federated machine learning to access the volume of data needed to transform healthcare with AI at scale, while respecting patient privacy and confidentiality
Federated learning lab
What is federated learning?

Federated learning creates endless possibilities for data scientists and researchers to work on emerging research questions and improve their models, trained across many diverse and representative datasets.

Models that are more accurate in their predictions also reduce healthcare cost for providers and insurers, which are under increasing pressure to provide value-based care with better outcomes.

Federated learning
How does federated learning work?

Federated learning collaboratively trains machine learning models in a distributed manner, without the need to exchange the underlying data. Algorithms are dispatched to different data centers, where they train locally.

Once trained, only the algorithm returns to the central location, not the data it trained on. At this point, the improved predictions are then sent to each local dataset to retain and improve.