Federated Learning Physical AI Oncology Trials Unification

The transition from conventional software-only artificial intelligence to physical AI systems incor- porating robotic hardware in oncology clinical trials represents a paradigm shift requiring unified infrastructure for privacy, regulation, cross-framework interoperability, and multi-organization coop- eration. This paper presents the PAI Oncology Trial FL platform (v1.1.0), a comprehensive federated learning framework comprising 235 Python modules (∼86,800 lines of code) that unifies five critical infrastructure pillars: (1) Privacy Infrastructure implementing all 18 HIPAA Safe Harbor identifiers with HMAC-SHA256 pseudonymization, (2) Regulatory Infrastructure spanning FDA, IRB, ICH- GCP, and multi-jurisdiction compliance across v0.6.0 and v0.9.1, (3) Cross-Framework Unification bridging NVIDIA Isaac Sim, MuJoCo, Gazebo, and PyBullet simulation environments, (4) Stan- dards & Benchmarking for Q1 2026 objectives including model conversion and registry pipelines, and (5) Multi-Organization Cooperation enabling federated training across academic medical cen- ters, community hospitals, and pharmaceutical companies. End-to-end workflow demonstrations are presented across 31 example scripts, 6 agentic AI production examples implementing Model Context Protocol (MCP), ReAct reasoning, real-time monitoring, autonomous orchestration, safety- constrained execution, and RAG-based compliance. A triple AI peer review process (v0.9.4–v0.9.9) using sequential Codex-to-Claude Code review-fix cycles resolved 31/31 code recommendations at 100% completion, establishing a dual-manufacturer trust benchmark for AI-generated clinical trial software. The platform demonstrates that unified federated learning infrastructure is a necessary precondition for transitioning the oncology industry to using robots in physical AI clinical trials.