Rhino Federated Computing, a Tel Aviv-based AI infrastructure company, has raised $15 million in Series A funding to scale its federated learning platform across healthcare systems globally. The round was led by AlleyCorp and LionBird, with participation from Fusion Fund, Arkin Digital Health, Qiming Venture Partners USA, TELUS Global Ventures (TGV), Wilson’s Bird Capital, Frank Sica, and Gaingels.

Founded by Ittai Dayan and Yuval Baror, Rhino is on a mission to bring AI to sensitive and siloed data environments - starting with healthcare. Its federated learning platform allows organizations to build shared machine learning models without ever moving or centralizing data, enabling breakthroughs in privacy-preserving collaboration.

As healthcare systems wrestle with data fragmentation, regulation, and ethical concerns, Rhino is building the infrastructure to make AI trustworthy, compliant, and decentralized by design.

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What Rhino Does

Rhino’s platform enables hospitals, pharma companies, and research networks to collaborate on AI models without sharing raw patient data. Instead of pooling datasets into a centralized location (which is risky and often non-compliant), Rhino deploys the model to each data holder’s environment, trains it locally, and then aggregates only the updated parameters back.

Key capabilities include:

• Federated learning orchestration across multi-institution networks
  
• Differential privacy and advanced encryption for secure model updates
  
• Plug-and-play deployment into hospital systems and EHRs
  
• Audit-ready compliance with HIPAA, GDPR, and regional data laws
  
• Pre-built ML pipelines tailored to medical imaging, genomics, and predictive care
  

It’s AI, without the data leaving the building.

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Where Most Founders Miss the Real Unlock

A lot of health tech and AI infrastructure startups frame themselves as “compliant” or “secure,” but that’s table stakes. Rhino realized something more nuanced: the real constraint isn't technology - it’s trust across fragmented institutions.

So instead of building a “tool for federated learning,” Rhino architected a system that could operate within misaligned incentives, uneven resources, and slow-moving governance structures. That’s where the magic is.

Here’s a powerful insight for any founder building in a complex, regulated, or multi-stakeholder space:

Don’t just remove technical friction. Remove narrative friction.

Investors didn’t write checks because Rhino had better encryption - they leaned in because the story aligned with system-wide urgency. Rhino wasn’t just a model runner; it was a trust broker. It offered every stakeholder a way to participate in the AI future without political risk, legal exposure, or reputational damage.

When founders start thinking in terms of consensus clearance, not just product performance, they create pathways for adoption that competitors never even see.

That’s what Rhino nailed - and why this $15M round was never just about data science. It was about infrastructure that earns agreement.

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Why This Changes the Game

Most AI models in healthcare fail to scale because the data never leaves its silo. Privacy laws, fragmented infrastructure, and trust gaps make it nearly impossible to train robust, generalizable models across diverse populations.

Rhino’s federated learning approach turns this on its head. By letting the model come to the data, they enable multi-center AI at scale - without compromising patient privacy or compliance.

And here’s what many founders miss when building in regulated industries: Technology alone doesn’t win. Friction reduction does.

Rhino didn’t just pitch “better AI.” They pitched smoother collaboration. Hospitals and data custodians don’t adopt tools because of a benchmark or white paper - they adopt because it helps them say “yes” faster inside their bureaucracy.

By engineering for legal, IT, and compliance workflows - not just data science workflows - Rhino positioned themselves as the missing infrastructure layer that allows healthcare AI to finally move from the lab into production.

This is the lesson: You’re not just building a product. You’re building permission. The permission to collaborate, to deploy, and to scale in systems that were designed to move slowly. Rhino made their go-to-market strategy not about feature sets - but about clearance paths. And that’s how they unlocked $15M from both healthcare-native and deep-tech investors.

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Market Outlook: Federated AI and Healthcare Intelligence on the Rise

The tailwinds behind Rhino’s model are strong - and accelerating.

• The federated learning market is projected to grow from $117 million in 2023 to $210 million by 2028, at a CAGR of 12.2%, driven by rising privacy requirements across healthcare, finance, and defense (MarketsandMarkets).
  
• The global AI in healthcare market is forecasted to reach $187 billion by 2030, up from $15.1 billion in 2022, growing at a CAGR of 37.5%, fueled by diagnostics, drug discovery, and personalized medicine (Precedence Research).
  
• A recent Deloitte survey found that 72% of U.S. healthcare executives are planning to adopt AI, but cite “data fragmentation and patient privacy” as the top two barriers to implementation.
  
• In parallel, governments across the U.S., EU, and Asia-Pacific are passing legislation that mandates data localization, further accelerating demand for privacy-preserving AI infrastructure.
  

As more AI regulation (such as the EU AI Act and FDA’s AI/ML guidance) begins to take effect, federated learning will no longer be optional - it will be the compliance-first foundation for trustworthy medical AI.

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What’s Next for Rhino Federated Computing

With fresh capital in hand, Rhino plans to:

• Expand across North America and Europe, onboarding leading academic medical centers
  
• Grow its engineering and privacy research teams, focusing on next-gen encryption and distributed optimization
  
• Build strategic partnerships with pharma, insurers, and clinical research organizations
  
• Launch new product modules for real-time federated inference and cross-site model explainability
  
• Secure key certifications and regulatory pathways for clinical deployment in the U.S., EU, and Canada
  

Rhino’s long-term vision is to become the de facto infrastructure for collaborative, privacy-safe AI - first in healthcare, then across industries like finance, insurance, and public sector analytics.

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Why This Moment Matters

As AI becomes more capable, the battlefront shifts from what we can predict to what we are allowed to predict. Rhino Federated Computing is defining the rules of engagement for data collaboration in a privacy-first world.

They’re not trying to centralize intelligence - they’re trying to decentralize trust.

And that’s a future the world is ready for.