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Rhino Federated Computing, a startup specializing in AI collaboration tools for regulated sectors, has successfully raised \$15 million in its Series A funding round. The funding, which brings its total to over \$30 million, will help the company expand its federated AI platform. The investment round saw contributions from both returning and new investors, including AlleyCorp, LionBird, Fusion Fund, and several others.
Founded by Dr. Ittai Dayan, a former AI leader at Mass General Brigham, and serial entrepreneur Yuval Baror, Rhino is addressing a fundamental challenge in the AI space—how to harness the value of sensitive data without violating privacy laws or security standards.
Federated AI, which is central to Rhino’s vision, allows multiple institutions to build and train AI models collaboratively without needing to share or centralize the underlying data. This approach makes it especially suitable for industries that deal with sensitive information, such as healthcare, biopharma, and finance. Rhino’s platform is already being used in several high-profile projects, such as collaborations with Sheba Medical Center and biopharma consortia for generative AI models.
This funding comes at a time when large AI platforms like Google and OpenAI are pushing the boundaries of data access and control, raising concerns about compliance, privacy, and intellectual property protection in highly regulated fields. Rhino’s federated AI technology is positioned as a viable alternative to centralized models, offering a way to scale AI capabilities while staying compliant with data protection regulations.
What Undercode Says:
Rhino Federated Computing is advancing the future of AI by introducing a unique solution that addresses one of the industry’s most critical challenges: privacy and data security. As AI becomes increasingly pervasive in sectors such as healthcare and finance, organizations must balance innovation with strict regulatory requirements. Traditional methods of data sharing—where information is centralized—have become problematic due to concerns over data security breaches, patient privacy, and intellectual property theft.
Federated AI, which underpins
In healthcare, for instance, federated learning could enable hospitals and research institutions to develop AI models for disease diagnosis without ever sharing patient data, ensuring compliance with HIPAA regulations and other privacy laws. Similarly, in finance and biopharma, companies can work together to develop AI tools for fraud detection or drug discovery while safeguarding confidential information. This level of collaboration has the potential to unlock breakthroughs in these industries, which have long been hindered by data privacy concerns.
Rhino’s strategy of leveraging federated AI makes it particularly well-positioned in today’s landscape, where trust and security are essential to adoption. As more organizations seek scalable AI solutions, especially in regulated industries, the need for platforms that ensure compliance with privacy regulations will only continue to grow. The \$15 million funding will allow Rhino to scale its technology, enabling it to expand its reach into new markets and industries that require robust data protection protocols.
Rhino’s platform is also noteworthy for its ability to operate across different jurisdictions, further emphasizing its potential to support international collaborations while adhering to local data protection laws. This is particularly important in industries like biopharma and finance, where cross-border data sharing and collaboration are often necessary but complicated due to varying regulations.
The company’s partnerships with major institutions, including Sheba Medical Center and Google, showcase the practical applications of its federated AI system. As more players in the AI and technology space recognize the importance of data security, Rhino’s federated model could become the standard for responsible AI development across industries.
Fact Checker Results:
Rhino’s federated AI technology indeed provides a secure and privacy-compliant alternative to centralized data-sharing models, making it suitable for regulated industries. ✅
The
Rhino’s \$15 million Series A funding is set to expand its federated AI platform and accelerate the adoption of its technology across more industries. ✅
Prediction:
As the demand for AI solutions continues to surge, federated AI models like Rhino’s are likely to become the go-to framework for industries where data privacy is paramount. In the next few years, we can expect to see more startups and established companies adopting federated learning to comply with stringent regulations, especially in healthcare, finance, and biopharma. Rhino’s expansion, fueled by its recent funding, positions it to lead this transition, making its federated AI platform a key enabler of responsible, scalable AI innovation.
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Reported By: calcalistechcom_1aa01d44e93aee299b3b2199
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