Rhino Federated Computing

What Rhino Federated Computing does

Rhino Federated Computing solves the challenge of seamlessly connecting siloed data through federated computing. The Rhino Federated Computing Platform (RhinoFCP) acts as a 'data collaboration tech stack,' providing computing resources, data preparation and discoverability, and model development and monitoring in a secure, privacy-preserving environment. It offers flexible architecture (multi-cloud and on-prem hardware), end-to-end data management workflows (multimodal data, schema definition, harmonization, and visualization), privacy-enhancing technologies (e.g., differential privacy), and allows for the secure deployment of custom code and third-party applications via persistent data pipelines. Rhino enables organizations to process, harmonize, analyze, and build AI on distributed data while maintaining enterprise-grade controls for privacy, security, compliance, and responsible AI by bringing computation to the data rather than centralizing it.

Products

• Rhino Federated Computing Platform (RhinoFCP)A secure, scalable software solution for federated learning and collaborative data processing. It connects to existing data sources, processes data locally to extract federated insights, and ensures data remains secure behind the data custodian's firewall. It features an intuitive web interface or extensible SDK/API for connecting disparate data sources, processing custom workloads, and scaling. It also includes federated MLOps for managing the full AI model lifecycle.
• Secure MCP ServerThe world's first secure MCP for federated computing, allowing users to explore data across sites, perform semantic and syntactic data harmonization, train models, and run inference using plain language without writing code.
• AutoMapperA GenAI-enabled tool within Rhino FCP that streamlines data harmonization with human-in-the-loop validation, ensuring data stays behind the data custodians' firewalls.
• Federated DatasetsAn application within Rhino FCP that enables exploration of and collaboration with data distributed across multiple partners, facilitating secure and efficient data analysis while maintaining data privacy by sharing only AI model characteristics (like weights and gradients), not raw data.
• Secure Access (aka "Secure Remote Desktop") FeatureA feature that allows third parties to review and annotate images without taking possession of the images, ensuring both images and annotations remain behind the custodian's firewall.
• RhinoDHE™ for FHIRA data transformation product leveraging GenAI to streamline data harmonization, allowing organizations to map local data models to target models or enable data SMEs to do so, with protections like RBAC, encryption, and audit logs. It includes intelligent data processing, workflow, and integrated monitoring.

Key capabilities

• Seamlessly connects siloed data through federated computing
• Secure, privacy-preserving environment
• Flexible architecture (multi-cloud and on-prem hardware)
• End-to-end data management workflows (multimodal data, schema definition, harmonization, visualization)
• Privacy enhancing technologies (e.g., differential privacy, k-anonymization, homomorphic encryption, confidential computing)
• Secure deployment of custom code & 3rd party applications via persistent data pipelines
• Federated Analytics: Explore and learn from privacy-protected data with granular control
• Federated Inference: Run models across data sites on a continuous basis for real-time decisions at the edge
• Federated Learning: Build better models with diverse data, train models and run inference with a single click, harmonize data across sites
• Compliant by Design: Audit-ready logging across every action and activity
• Multi-Level Security and Privacy Protection: Differential privacy, enterprise key management, confidential computing
• Open and Agnostic: Bring your own cloud, bring your own tech stack
• Centralized control with decentralized execution
• Intuitive web interface or extensible SDK/API
• AI-enabled tools for data harmonization (AutoMapper)
• Granular access control (Role-Based Access Control - RBAC)
• Local data processing across major cloud providers or on-prem
• Secure, flexible code deployment in privacy-enforcing sandboxes
• Federated MLOps for AI lifecycle management
• Full accountability: RBAC, encryption with customer-managed keys, comprehensive audit logs
• Global collaboration with local compliance (ISO 27001, SOC 2 Type II, HIPAA, GDPR)
• 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 models, not deployment across clouds and on-prem customers
• Segment: Drive customer expansion and upsell options on a model-by-model basis
• Inference at the Edge: Client runs model against local data, only outputs return
• Federated Learning: Updated model weights return to orchestrator from client sites to improve master model
• Private Fine-Tuning: Client creates derivative model versions on their own site without access to fine-tuned model or data
• Unified intelligence layer across sites for agent fleet
• Unlock data previously off-limits without changing governance posture
• Go from pilot to production without each data site standing up its own agentic infrastructure
• Govern and observe across all sites from a single control plane
• Develop agent workflows in any framework, deploy to client nodes at each data site
• Run performance assessment across sites under federated conditions, subject matter experts score agent outputs locally
• Production agents run continuously at each site with centralized observability

Use cases

• Unlocking siloed data for federated analytics and learning
• Eliminating barriers to collaboration
• Maintaining data security, privacy, and sovereignty
• Sharing insights, not raw data
• Reducing time to value for data collaborations
• Working with data that cannot be centralized
• Compressing negotiation times for data access
• Building models or running analysis on larger, more diverse, multi-modal datasets
• Satisfying data governance, privacy regulations, IP protection, and contractual obligations
• Exploring and learning from privacy-protected data
• Running models across data sites on a continuous basis
• Enabling real-time decisions at the edge
• Extending the operational reach of AI into new domains
• Building better models with more diverse data
• Training models and running inference with a single click
• Harmonizing data across sites to streamline data prep
• Proactive Surveillance: Analyzing real-time health data securely, detecting disease signals and linking datasets without transferring sensitive information
• Responsible AI Development: Securely training models on diverse, distributed datasets, ensuring privacy, reducing bias, and validating efficacy across populations
• Quality & Safety Measure Development: Enabling collaboration across sites to harmonize data, run analytics, and train models while ensuring data privacy
• Decentralized Data Repositories: Creating, maintaining, and provisioning access to multimodal datasets residing across multiple data custodian organizations without requiring data transfer
• Protecting model weights and intellectual property for model developers
• Protecting client data for enterprises
• Accelerating deal closure by removing data sharing obstacles
• Scaling model deployment across various cloud and on-prem environments
• Driving customer expansion and upsell options on a model-by-model basis
• Building agent workflows and deploying them to client nodes at each data site
• Running performance assessments for agents across sites under federated conditions
• Deploying production agents continuously at each site with centralized observability
• Building persistent RWD Networks with hospitals, biobanks, and other data partners for longitudinal analysis of multimodal datasets
• Collaborative AI Development: Partnering with biopharmas, biotechs, and model developers to train, fine-tune, and validate AI models while protecting IP
• Bridging Internal Silos: Working with data distributed across geographies or cloud providers as if it was centralized
• Financial crime fighting consortia: Enabling complex, confidential collaboration among multiple organizations (banks, regulators, law enforcement) where data sharing is challenging
• Improving models with 3rd party data for credit scoring, capital markets, marketing, etc.
• Macroeconomic Monitoring: Generating real-time insights for regulators, credit rating agencies, risk managers, and investors
• Advancing multi-institutional oncology research, e.g., pancreatic cancer detection
• Crowdsourcing Medical Image Annotation: Enlisting clinical experts to annotate medical images for AI model training and validation without data movement
• Using Large Language Models (LLMs) for Clinical Notes Analysis to find early signs of diseases

AI approach

Rhino Federated Computing solves the challenge of connecting siloed data through federated computing to enable AI development and analytics. Their platform, RhinoFCP, facilitates data preparation, discoverability, model development, and monitoring in a secure, privacy-preserving environment. It supports federated analytics, inference, and learning, allowing models to be built and run on diverse, distributed datasets without centralizing raw data. They leverage GenAI for data harmonization (AutoMapper, RhinoDHE) and support various AI frameworks. The company was founded following a landmark global study demonstrating the power of Federated Learning.

Tech named: pytorch, tensorflow, sklearn, LLMs, GenAI

Industries served

• Software Development
• Healthcare
• Life Sciences
• Financial Services
• Public Sector
• Biopharma
• Oncology Research

What it says sets it apart

• Seamlessly connects siloed data through federated computing
• Maintains data security, privacy, and sovereignty by sharing insights, not raw data
• Data is never transferred, always in control at the edge
• Compliant by Design with audit-ready logging
• Multi-Level Security and Privacy Protection including differential privacy, enterprise key management, confidential computing
• Open and Agnostic, allowing customers to bring their own cloud and tech stack
• Centralized control with decentralized execution
• Gen-AI leverage for data engineers and data SMEs (AutoMapper, RhinoDHE)
• Granular access control (RBAC)
• Local data processing across various environments (cloud and on-prem)
• Secure, flexible code deployment in privacy-enforcing sandboxes
• Federated MLOps for streamlined AI lifecycle management
• Full accountability with comprehensive audit logs and customer-managed keys
• Adherence to rigid security and privacy standards: ISO 27001, SOC 2 Type II, HIPAA, GDPR
• Enables model developers to protect their weights and IP while clients protect their data
• Accelerates collaborations from months to weeks
• Allows for exploration of data across sites, semantic and syntactic data harmonization, model training, and inference without writing code (Secure MCP Server)
• Enables secure annotation of images by third parties without data transfer (Secure Access Feature)
• Transforms research with privacy-preserving AI, enabling breakthroughs in medical fields like pancreatic cancer detection
• Addresses the risk of data re-identification in traditional data-sharing methods

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.