Federated Model Networks
Valuable models. Sensitive data. Private by design.
Deploy proprietary AI models at enterprise scale — without shipping weights, receiving client data, or bespoke infrastructure per customer.
The Problem
Every AI model business relationship has the same structural impasse. The model developer needs to protect their weights. The enterprise client needs to protect their data. Traditionally, one party has to blink — the vendor ships weights and loses control of their IP, or the client sends proprietary data and takes on legal and competitive risk. In most cases, the relationship simply does not happen, not because the value is absent, but because neither party is willing to accept the exposure required to capture it.
Why Rhino
Protect Models
Deploy models to client environments without transferring weights or source code
Accelerate
Remove data sharing obstacles and drive deals to close more quickly
Scale
Invest in improving your models, not deployment across clouds and on-prem customers
Segment
Drive customer expansion and upsell options on a model-by-model basis
Relationship Modes
Inference at the Edge — Client runs your model against local data. Only outputs return. Client data never leaves their respective environments.
Federated Learning — Updated model weights return to your orchestrator from all client sites to improve your master model. No client data moves.
Private Fine-Tuning — Client creates derivative model versions on their own site. You never access the fine-tuned model or the data used to create it.
Platform Power
Rhino’s Federated Computing Platform undergirds every solution and customer success we deliver.
Compliant by Design
Audit-ready logging across every action and activity
Multi-Level Security and Privacy Protection
All the basics, and differential privacy, enterprise key management, confidential computing, and more thrown in
Open and Agnostic
Bring your own cloud, bring your own tech stack
Secure MCP Server
We just launched the world's first secure MCP for federated computing. Use plain language to explore data across sites, perform semantic and syntactic data harmonization, train models, and run inference – all without writing a single line of code.
