Rhino Federated Computing

Provides the Rhino Federated Computing Platform (FCP), a secure, scalable software solution for federated learning and collaborative data processing with no centralized data storage or transfer.

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Founded: Not disclosed
Headquarters: Not disclosed
Company size: Not disclosed
Products tracked: 5

Products by Rhino Federated Computing

5 products

RhinoDHEData Harmonization Engine using GenerativeAI to automatically harmonize data with human-in-the-loop validation while ensuring data stays behind the data custodians' firewalls.0.00 reviewsData Integration & ETL

Rhino Federated Computing PlatformSecure, scalable software solution for federated learning and collaborative data processing with no centralized data storage or transfer.0.00 reviewsMLOps & Deployment

Federated Data ScienceRun the full data science lifecycle—exploration, data preparation, analysis, model training, validation, and inference—across distributed data environments with full control and...0.00 reviewsMLOps & Deployment

Federated Model NetworksDeploy proprietary AI models at enterprise scale without shipping weights, receiving client data, or bespoke infrastructure per customer.0.00 reviewsMLOps & Deployment

Federated Agentic AIBuild, evaluate, and deploy AI agents on data that must stay where it lives.0.00 reviewsMLOps & Deployment

Pricing for Rhino Federated Computing

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Contact Rhino Federated Computing for current plans, volume pricing, and enterprise terms.

Trust profile

Security and compliance

ISO/IEC 27001

SOC 2 Type II

HIPAA

GDPR

role-based access control (RBAC)

encryption with customer-managed keys

differential privacy

k-anonymization

homomorphic encryption

confidential computing

enterprise key management

audit-ready logging

secure containerized code deployment

Latest updates from Rhino Federated Computing

Jun 182026

Company news

Rhino Federated Computing Discusses Federated Learning Adoption in Clinical Trials

During a video interview at the 2026 DIA Global Annual Meeting, CEO Ittai Dayan of Rhino Federated Computing discussed the role of federated learning in overcoming data fragmentation in clinical trials. He emphasized that the technology reduces friction by enabling data analysis without centralized pooling, provided that strong incentive structures for collaboration are already in place. Dayan highlighted the Eli Lilly TuneLab program as an example where shared data leads to improved AI applications for all participants, and outlined requirements for successful enterprise-level deployment.

CustomerEli Lilly