The future of collaboration starts here.

Federated learning

Federated learning is a new decentralized machine learning procedure to train machine learning models with multiple data providers. 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.

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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 solve the problem of patient privacy and data protection. A successful implementation of federated learning 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.

Solving the biggest problem in medical research

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How does federated learning solve the main challenges of machine learning in healthcare?

Machine learning has the potential to revolutionize all industries, including healthcare. It can do this by accelerating medical research using its ability to generate medical insights (from cancer biomarker identification to patient screening and genetic prediction from imaging).  These applications not only strengthen researchers’ abilities to make discoveries, they also help address time and cost challenges across the healthcare industry.

However, machine learning approaches are “data hungry”. Algorithms need access to large and diverse datasets to train, improve their accuracy and eliminate bias.

Today's standard approach of centralizing data from multiple centers must be balanced with critical concerns regarding patient privacy and data protection. Software that handles personal data is bound by strict privacy laws. Healthcare systems must protect personal data at all times, and current standard practices such as anonymization may even require removing data that could be critical for medical discoveries.


Scientist working with model

How can researchers access the volume of data needed to transform healthcare with AI at scale, while respecting patient privacy and confidentiality?

Hospitals, research centers and biopharma companies need to start collaborating with each other.

Federated learning powers next generation AI in healthcare

Federated learning technology 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.

How does it 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.

Federated learning powers next generation AI in healthcare

Federated learning technology 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.

How does it 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.

Federated learning software

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