Highly Secure, Scalable Collaboration
The Rhino Federated Computing Platform (Rhino FCP) is a secure, scalable software solution for federated learning and collaborative data processing.
No centralized storage, no data transfer
Rhino FCP easily connects to existing data sources and builds upon on the strong data privacy and security standards already in place. As data are processed locally to extract federated insights, data remain secure behind the data custodian's firewall, streamlining collaboration agreements and requiring no data transfers.
Centralized control with decentralized execution
Rhino FCP features an intuitive web interface or extensible SDK/API that guides users through connecting disparate data sources, processing custom workloads, and scaling up to add newly discovered data sources or deploy new workloads at the edge.
Connect
Easily connect to data sources
Rhino FCP guides users through the steps of connecting to decentralized data sources across any cloud or on-prem.
Harmonize disparate data sets
AI-enabled tools standardize data models, simplifying data preparation and accelerating time to insight.
Granular access control
Role-based access control (RBAC) is configurable to ensure privacy and security while governing all access to data and workloads
Process
Federated statistics, federated learning, and federated inference
Execute cross-silo analytics; use existing models (including LLMs) with frameworks like pytorch, tensorflow, sklearn or others to train a federated model; or run inference with models on partners' data while protecting your IP.
Built-in data sovereignty and privacy
Rhino FCP enables local data processing across environments hosted by all major cloud providers or at on-prem data centers. The platform uses privacy-enhancing techniques such as differential privacy, k-anonymization, and homomorphic encryption.
Secure, flexible code deployment
Custom code is deployed at the source of the data in secure containers, providing a privacy-enforcing sandbox to prevent data leakage.
Scale
Streamline AI lifecycle management
With Rhino’s federated MLOps, users can easily manage the full life cycle of AI models, including pre-processing, training, validation, or fine-tuning, as well as integrate common third-party tools to manage models across distributed environments.
Full accountability
RBAC, encryption with customer-managed keys and comprehensive audit logs, protect complex code, model parameters, and data environments and provide full project transparency and accountability.
Global collaboration with local compliance
Rhino FCP adheres to rigid security and privacy standards: ISO 27001, SOC 2 Type II, HIPAA, and GDPR.
