Rhino FCP

The Rhino Federated Computing Platform (Rhino FCP) is a secure, scalable software solution designed for federated learning and collaborative data processing in life sciences and beyond. It enables organizations to advance AI initiatives with confidence by connecting to decentralized data sources without requiring centralized data storage or any data transfers, keeping data securely behind each data custodian's firewall at all times.

Rhino FCP is built for data engineers, data subject matter experts, and organizations seeking to collaborate across institutional boundaries while maintaining strict data privacy and sovereignty. The platform combines an intuitive web interface with an extensible SDK and API, allowing users to connect disparate data sources, process custom workloads, and scale their federated networks across cloud and on-premises environments.

Data Connectivity and Harmonization

• Guides users through connecting to decentralized data sources across any cloud provider or on-premises infrastructure.
• AI-enabled tools standardize data models to simplify data preparation and accelerate time to insight.
• AutoMapper leverages generative AI to streamline data harmonization with human-in-the-loop validation, ensuring data remains behind data custodians' firewalls throughout the process.

Federated Analytics and AI Capabilities

• Supports federated statistics, federated learning, and federated inference for cross-silo analytics.
• Compatible with existing models, including large language models (LLMs), and major frameworks such as PyTorch, TensorFlow, and scikit-learn for training federated models.
• Enables inference on partners' data while protecting proprietary model intellectual property.
• Federated MLOps capabilities allow users to manage the full AI model lifecycle, including pre-processing, training, validation, and fine-tuning, with integration of common third-party tools across distributed environments.

Security, Privacy, and Access Control

• Role-based access control (RBAC) is fully configurable to govern all access to data and workloads, ensuring privacy and security.
• Custom code is deployed at the data source within secure containers, providing a privacy-enforcing sandbox that prevents data leakage.
• Privacy-enhancing techniques include differential privacy, k-anonymization, and homomorphic encryption.
• Encryption with customer-managed keys and comprehensive audit logs provide full project transparency and accountability.
• Local data processing is supported across all major cloud providers and on-premises data centers, ensuring data sovereignty.

Compliance and Governance

• Rhino FCP adheres to rigorous security and privacy standards, including ISO 27001, SOC 2 Type II, HIPAA, and GDPR.
• The platform streamlines collaboration agreements by eliminating the need for data transfers, reducing regulatory and contractual complexity.
• Comprehensive audit logs and RBAC protect complex code, model parameters, and data environments across all participating organizations.

Rhino FCP supports deployment across all major cloud environments as well as on-premises data centers, making it suitable for global collaborations that require local compliance. Its architecture of centralized control with decentralized execution enables organizations to build scalable federated networks while meeting the highest standards of data privacy and security.