Rhino Federated Computing

About Rhino Federated Computing

Rhino Health provides a federated compute platform that utilizes federated learning and edge computing to enable secure, privacy-preserving access to healthcare data across multiple institutions. This approach significantly reduces project setup times from months to days while ensuring compliance with data privacy regulations, allowing AI developers to efficiently train models without exposing sensitive information.

```xml <problem> Healthcare data is often siloed across institutions, making it difficult for AI developers to access diverse datasets needed to train robust and generalizable models. Sharing sensitive patient information across institutions poses significant privacy and compliance risks, hindering collaborative research and AI innovation in healthcare. Traditional methods of data sharing are slow, cumbersome, and require extensive data engineering and IT resources. </problem> <solution> Rhino Health offers a federated computing platform that enables secure, privacy-preserving access to healthcare data across multiple institutions. By integrating federated learning and edge computing, the platform allows AI models to be trained on distributed datasets without exposing raw data or transferring it across institutional boundaries. This approach reduces project setup times from months to days, streamlines data harmonization, and ensures compliance with data privacy regulations such as HIPAA and GDPR. The platform provides AI innovators with an environment for data harmonization, image annotation, exploratory analysis, federated training/inference, and application development. </solution> <features> - Federated computing platform integrating federated learning and edge computing - Secure, privacy-preserving access to distributed healthcare data - Data harmonization tools for aligning disparate data sources - Image annotation capabilities for enhancing imaging data - Exploratory analysis tools for uncovering novel insights - Federated training and inference for building robust AI models - Application development environment for creating custom applications - LLM-driven Harmonization Copilot for advanced data harmonization - Federated Datasets for seamless multi-site analytics - Compliance with ISO 27001, SOC 2, HIPAA, and GDPR </features> <target_audience> The primary target audience includes AI developers, data scientists, and researchers in healthcare and life sciences who require access to diverse datasets for training AI models while adhering to strict data privacy and compliance regulations, as well as hospitals and research institutions seeking to collaborate on AI projects without compromising data sovereignty. </target_audience> ```

What does Rhino Federated Computing do?

Rhino Health provides a federated compute platform that utilizes federated learning and edge computing to enable secure, privacy-preserving access to healthcare data across multiple institutions. This approach significantly reduces project setup times from months to days while ensuring compliance with data privacy regulations, allowing AI developers to efficiently train models without exposing sensitive information.

Where is Rhino Federated Computing located?

Rhino Federated Computing is based in Boston, United States.

When was Rhino Federated Computing founded?

Rhino Federated Computing was founded in 2020.

How much funding has Rhino Federated Computing raised?

Rhino Federated Computing has raised 15000000.

Location
Boston, United States
Founded
2020
Funding
15000000
Employees
36 employees
Major Investors
AlleyCorp

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Rhino Federated Computing

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Executive Summary

Rhino Health provides a federated compute platform that utilizes federated learning and edge computing to enable secure, privacy-preserving access to healthcare data across multiple institutions. This approach significantly reduces project setup times from months to days while ensuring compliance with data privacy regulations, allowing AI developers to efficiently train models without exposing sensitive information.

rhinohealth.com7K+
cb
Crunchbase
Founded 2020Boston, United States

Funding

$

Estimated Funding

$10M+

Major Investors

AlleyCorp

Team (30+)

No team information available.

Company Description

Problem

Healthcare data is often siloed across institutions, making it difficult for AI developers to access diverse datasets needed to train robust and generalizable models. Sharing sensitive patient information across institutions poses significant privacy and compliance risks, hindering collaborative research and AI innovation in healthcare. Traditional methods of data sharing are slow, cumbersome, and require extensive data engineering and IT resources.

Solution

Rhino Health offers a federated computing platform that enables secure, privacy-preserving access to healthcare data across multiple institutions. By integrating federated learning and edge computing, the platform allows AI models to be trained on distributed datasets without exposing raw data or transferring it across institutional boundaries. This approach reduces project setup times from months to days, streamlines data harmonization, and ensures compliance with data privacy regulations such as HIPAA and GDPR. The platform provides AI innovators with an environment for data harmonization, image annotation, exploratory analysis, federated training/inference, and application development.

Features

Federated computing platform integrating federated learning and edge computing

Secure, privacy-preserving access to distributed healthcare data

Data harmonization tools for aligning disparate data sources

Image annotation capabilities for enhancing imaging data

Exploratory analysis tools for uncovering novel insights

Federated training and inference for building robust AI models

Application development environment for creating custom applications

LLM-driven Harmonization Copilot for advanced data harmonization

Federated Datasets for seamless multi-site analytics

Compliance with ISO 27001, SOC 2, HIPAA, and GDPR

Target Audience

The primary target audience includes AI developers, data scientists, and researchers in healthcare and life sciences who require access to diverse datasets for training AI models while adhering to strict data privacy and compliance regulations, as well as hospitals and research institutions seeking to collaborate on AI projects without compromising data sovereignty.

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