Activating the World’s Health Data with Federated Computing
Streamlining end-to-end healthcare research and AI development

At Rhino Health, we transform healthcare AI by integrating Edge Computing and Federated Learning into a cohesive Federated Computing strategy. This innovative approach provides AI developers with swift and secure access to healthcare data, dramatically reducing setup times from months to days and ensuring data privacy across global networks.


With the  Federated Computing Platform, AI innovators find an unmatched environment for:

Data harmonization
Aligning disparate data sources for uniform analysis.
Image annotation
Enhancing imaging data with precise, valuable notations.
Exploratory analysis
Delving deep into datasets to uncover novel insights.
Federated training/inference
Building robust AI models across a network of data custodians.
Application development
Crafting custom applications that operate on partner data, employing popular tools like Jupyter Notebook and TensorFlow for seamless integration into existing workflows.

Our federated multi-cloud extends the capabilities of ‘intelligent edge’ networks, ensuring data privacy and security while providing enduring connections to harmonized datasets facilitated by our advanced LLM-driven Harmonization Copilot. This enables continuous collaboration and innovation through our Federated Datasets, which fosters an ecosystem where data remains local, yet insights become global.

Core Capabilities

Why choose Rhino Health?

Federated Computing  is the best way to collaborate on healthcare data

Data sovereignty

Data custodians maintain full control over their data by keeping it within their network. This reduces regulatory hurdles associated with cross-jurisdiction data transfer and makes it easier to maintain full data lineage.

Operational efficiency

Reduce the data engineering and IT burden for data custodians and accelerate the completion of projects through central orchestration of projects with processing at the edge.

Diverse datasets

Leverage a broader range of high-quality, diverse datasets from multiple institutions, enhancing the robustness and generalizability of your AI models through federated collaboration.

Discover the power of Rhino Health’s Federated Computing Platform

Unlock insights with ease

Explore the full potential of data with our Federated Datasets—your gateway to seamless multi-site analytics.

Centralize your innovation workflow

Streamline your AI journey from development to deployment with a unified platform, navigating the complexities of distributed data with unmatched simplicity.

Compliance without compromise

Embrace innovation while adhering to rigorous security and privacy standards, including ISO 27001, SOC 2, HIPAA, and GDPR compliance. Your data remains secure under the vigilant protection of data custodians, allowing you to focus on driving impactful change.

Tools tailored for trailblazers

Elevate your team’s productivity with groundbreaking tools like our Harmonization Copilot and Federated Datasets, designed for the needs of data engineers and scientists.

Use Cases
The Rhino Health Federated Network is uniting AI innovators across healthcare & life sciences

Rhino Health has built the largest global federated network of hospitals. Join over 50 leading global institutions in the US, EMEA, and APAC that are reshaping healthcare & life sciences with Rhino Health. Innovators from around the world are making their data available for collaboration via our Federated Datasets and using the Federated Computing Platform to complete a range of research projects across data modalities and disciplines.

Join the Federated Network

Featured Insights & Highlights

Assuta Medical Centers and Rhino Health: A Pioneering Journey with Federated Datasets

Federated Computing and Rhino Health: Connecting the World's Data with Ittai Dayan

Federating a Quantile Regression Model for Neonatal Care at Tel Aviv (Sourasky) Medical Center Using Rhino Health’s Federated Computing Platform

Children’s National Hospital and Rhino Health Pave the Way for Enhanced Pediatric Diagnostics with Federated Learning

Democratizing Measure Development with Federated Computing

Increasing Transparency in AI Model Performance and Reducing Time to Insights with Distributed Registries for a Prominent Medical Society

Rhino Health's Federated Datasets feature enables Assuta to collaborate with industry partners to promote healthcare innovation while ensuring patient privacy is held to the highest standard. We have been able to accelerate collaborations from months to weeks.”

Daniel Rabina, Chief Innovation Officer, Assuta Medical Centers

Federated Datasets help builders like me quickly and efficiently search for available datasets in order to deploy and validate new AI solutions while preserving privacy and compliance.”

Satish Viswanath, Associate Professor at  Case Western Reserve University

As a model developer, I always need more Real-world Data to validate and refine my AI models. Federated Datasets help me search for and obtain access to relevant datasets to promote my research.”

Chunnan Hsu, Scientist in AI Healthcare, San Diego

Rhino Health's Federated Computing Platform provided us with the secure and robust infrastructure needed to test our Quantile Regression AI model in a Federated way. With their support, we were able to simulate the development of predictive models for the care of our premature newborn patients based on data from different hospitals. Rhino Health's expertise in Edge Computing and Federated Learning streamlined our workflow and fortified the privacy and security of our patient data, which is paramount in our field. We look forward to continuing this fruitful partnership as we explore new frontiers in healthcare AI.”

Brenda Kasabe, Data Engineering Project Management, I-Medata AI Center at Tel Aviv (Sourasky) Medical Center

Discover the future with Rhino Health

Ready to see how our Federated Computing Platform can transform healthcare? Join the Federated Network and start working with our collaborators’ data today!

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