Collaborative AI Model Training with Rhino Federated Computing on AWS

This article explores how Rhino Federated Computing Platform (FCP) integrates with AWS to enable secure, privacy-preserving collaborative AI model training in healthcare and life sciences.

• Federated computing allows organizations to train AI models on distributed data without centralizing sensitive information
• Rhino FCP supports cloud and on-premises environments with end-to-end data management and customizable privacy controls
• Platform uses AWS KMS, Amazon Bedrock, and Amazon Q for encryption, AI workflows, and natural language querying
• Each client node runs on Amazon EC2 with secure TLS communication exchanging only aggregated statistics
• HIPAA, GDPR, ISO 27001, and SOC 2 Type II compliant with audit logging via CloudTrail and CloudWatch
• ELISE consortium used federated learning for breast cancer research across US and Israel without sharing raw data
• Eli Lilly's TuneLab platform enables biotech partners to access drug discovery models trained on proprietary research data

Rhino FCP on AWS enables healthcare organizations to collaborate on AI development while maintaining strict data privacy and regulatory compliance across distributed environments.