GA4GH-aligned Trustworthy Federated AI

This proposal addresses the deployment gap in federated learning for genomics, where frameworks like FLAN enable distributed model training but lack standardization, security, and interoperability required for real-world biomedical environments. As a result, federated learning systems remain difficult to deploy across institutions and cannot scale in regulated settings. To solve this, the project transforms FLAN into a GA4GH-aligned, production-ready federated AI system by integrating key standards across the stack. It incorporates DRS for secure and standardized data access, TES/WES for portable and reproducible task and workflow execution, and TRS for containerized tool discovery. Security is strengthened using GA4GH Passports and Attested TLS for zero-trust, identity-aware communication, while Model Context Protocol (MCP) is introduced to enforce privacy constraints and execution policies across federated nodes. The deliverables include a refactored FLAN pipeline with DRS-based data access, modular training workflows executed via TES, end-to-end orchestration using WES (CWL/WDL/Nextflow), containerized environments registered with TRS, integrated Passport-based authentication and Attested TLS communication, a prototype MCP-based policy enforcement layer, and comprehensive documentation with a GA4GH-compliant reference implementation.