Federated Learning (FL) offers a privacy-enhancing architecture for training artificial intelligence on decentralized healthcare data, yet the prevailing mantra to "move the model, not the data" obscures significant privacy risks, ethical dilemmas, and regulatory conflicts. This article challenges the assumption that FL inherently solves cross-border compliance by analyzing a hypothetical consortium involving the United States, the UK, EU, China, and Brazil. We identify a specific compliance deadlock arising from the friction between Western rights-based frameworks (HIPAA, GDPR, LGPD) and state-centric security models (China's PIPL/DSL), particularly regarding model inversion attacks and data localization. Moving beyond validatory analysis, we propose a multi-layered Federated Governance Framework to operationalize data diplomacy. This contribution introduces novel legal and structural mechanisms: the Federated Data Sharing Governance as a Service (GaaS) to neutralize conflicts of interest through third-party administration; and relational mechanisms, including blockchain-based dynamic consent to solve the cascade of consent problem. This framework provides a blueprint for converting theoretical FL potential into a legally compliant, ethically sound, and functioning international Learning Health System (LHS).
Alvarenga et al. (Thu,) studied this question.