A federated machine learning framework for physical AI oncology clinical trials. Enables multiple hospitals to collaboratively train AI models for tumor response prediction, surgical planning, and treatment optimization without sharing raw patient data. Integrates physical AI components including patient digital twins with multiple growth models, surgical robotic telemetry with clinical threshold evaluation, and multi-modal sensor fusion. Supports FedAvg, FedProx, and SCAFFOLD aggregation strategies with differential privacy, secure aggregation, role-based access control, breach response protocols, and multi-site enrollment management. Includes clinical analytics (PK/PD modeling, survival analysis, risk stratification, CDISC interoperability), regulatory submissions (eCTD compilation, compliance validation), FDA submission tracking, and HIPAA/GDPR/FDA 21 CFR Part 11 compliance.

If you use this software, please cite it using the metadata from this file.