Selected as Federated Learning Technology Partner to Power One of World’s Largest Precision Health Consortia

The collaboration will bring precision medicine and personalized care in cancer and neurodegenerative disease to Canadians through the application of big data and artificial intelligence

At, we are proud to partner with the Terry Fox Research Institute to deliver the Digital Health and Discovery Platform (DHDP), an initiative that will advance Canadian healthcare through cutting-edge AI technology and a world-leading data access and governance framework.  This powerful network illustrates the ways in which the platform can be used by a variety of organizations to safely collaborate and drive new insights on some of the world’s most sensitive data.  We care deeply about enabling breakthrough AI innovation in a safe way and we love our partners in all industries from insurance and financial services to healthcare; we are particularly excited to see our technology be put to use to help create new cancer treatments through the development of precision medicine.  

How Supports the DHDP

With its data evaluation platform, enables the DHDP to unlock data-driven discoveries for cancer and other diseases. 

DHDP’s software, powered by’s data evaluation platform, will be deployed at participating hospitals and research centres across Canada.  The platform uses federated learning technology to ensure privacy, accessibility and traceability of data, allowing users to build sophisticated machine learning models and learn collectively from data without ever sharing sensitive patient information.  Because the platform relies on federated learning technology and other privacy enhancing techniques, no data needs to move across borders or between different institutions. All the researchers on the platform can perform complex data science tasks, including building cutting edge AI models for use cases like tumor genome sequence analysis or predictive modeling for treatment response, without seeing or directly accessing the data from the other participants. is enabling the following technological approaches:

● Federated Learning Platform

To minimize the risks to patient privacy, the DHDP uses’s federated learning platform to support pan-Canadian research. Under this model, patient data remains at the site where it is generated, and never crosses any institutional, local or provincial borders.  The platform uses federated learning to essentially move the model to the data, instead of moving the data to the model.  In working with the DHDP, it was clear that their mission required multiple parties across multiple institutions and provinces to access each other’s data, and that the platform would be the perfect solution for this challenge because of the way we leverage federated learning technology and other privacy enhancing techniques to enable safe collaboration.  Alternative approaches relying on data movement across jurisdictions and organizations meaningfully increases the privacy, regulatory and security risks associated with this type of sensitive data and research. 

● Data Evaluation Product

Many of our customers use the platform to evaluate external datasets they are considering purchasing or partnering with to see how it might impact the models they have or want to build.  Once they gain confidence that the new dataset improves their model, they may then buy the data and onboard it once they productionize their model. In the case of the DHDP network, the participants can not only evaluate the impact of third party datasets on their research models, but they can productionize and use those datasets even after the models have been trained while keeping the data federated.  This is necessary because the data they are accessing is extremely sensitive healthcare data, and too much value would be lost if it needed to be transformed to the point where it could be moved across borders and among different institutions.  With the platform, that data never moves, so the participants in the network get to capture the full value of all the data integrated into their models, and are not forced to engage in a lengthy legal and compliance process since the platform is built by default to meet their privacy and security standards.

● Privacy Preserving

The DHDP also leverages’s privacy-protecting data governance framework and data science technologies, which play a crucial role in transforming collaborative health research, enabling researchers to access important data sets while protecting patient privacy.  A great example of this includes our use of differential privacy, which is commonly used by government statistics agencies to preserve important statistical patterns in data when sharing or capturing those patterns in a model without compromising the privacy of any individual in the dataset. We make this possible by adding noise to the information shared between the parties. This is important to enable cross-organizational research while holding the highest bar for privacy and security of patient information and other sensitive data.  

Our commitment extends beyond use of our leading federated data science platform; we are dedicated to actively collaborating with fellow DHDP partners to advance the mandate of the DHDP.  For anyone that wants to read more about this incredible project, please take a look at their announcement linked here. 

Click here to learn more

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