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Connecting the world's data with Federated Computing

AI developers face significant hurdles in training their models on sufficiently large and diverse datasets because of privacy, security, and cost concerns. The Rhino Federated Computing Platform (FCP) addresses all of these concerns.

The Rhino FCP unlocks healthcare data collaborations using edge computing and federated learning, leaving data at rest at each site while allowing data pre-processing, harmonization, model training and validation - all with no data transfer.

Members of the rhino network include dozens of medical centers, universities and industry leaders collaborating on a variety of data modalities such as medical imaging, medical notes, histopathology, genomics, and proteomics.

What is Federated Computing?

Federated Data

Data is mapped to common data models and harmonized

Federated Analytics

Privacy preserving data analysis and queries to facilitate data discovery

Federated Computing

Edge compute applications including federated learning


With Federated Computing, code is sent to each edge node, executed and results are aggregated for creating powerful AI models while preserving patient privacy.

Why use Rhino versus...

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Only use your own data


Limited generalizability and potential to translate into impact

Rhino FCP Solution

Privacy-preserving data collaborations across sites with diverse patient data

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Centralize data from different sources


Slow due to data sharing concerns, expensive due to creating replicates of massive data sets and low-quality due to non-standard data processing methods

Rhino FCP Solution

Data remains federated on the edge, eliminating risks and costs of centralization

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Build 'DIY' federation with open-source frameworks


Expensive, non-repeatable and non-scalable

Rhino FCP Solution

Start collaborating in days, not months, with an enterprise-hardened solution

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