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    Federated Learning (FL) faces privacy risks and regulatory conflicts in international healthcare data sharing. A new Federated Governance Framework is proposed to ensure compliance and ethical data use in global Learning Health Systems.

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    Area of Science:

    • Artificial Intelligence
    • Healthcare Informatics
    • International Law

    Background:

    • Federated Learning (FL) is presented as a privacy-preserving method for AI training on decentralized health data.
    • However, the "move the model, not the data" approach overlooks substantial privacy risks and regulatory challenges, especially in cross-border collaborations.
    • Existing frameworks struggle with the complexities of international data sharing and compliance.

    Purpose of the Study:

    • To critically analyze the privacy risks and regulatory conflicts inherent in international Federated Learning for healthcare.
    • To challenge the assumption that FL inherently ensures cross-border compliance.
    • To propose a novel governance framework for legally compliant and ethically sound international AI in healthcare.

    Main Methods:

    • Analysis of a hypothetical international consortium (US, UK, EU, China, Brazil) to identify compliance deadlocks.
    • Examination of conflicts between Western rights-based data protection laws (HIPAA, GDPR, LGPD) and state-centric security models (China's PIPL/DSL).
    • Proposal of a multi-layered Federated Governance Framework with specific legal and structural mechanisms.

    Main Results:

    • Identified a compliance deadlock stemming from the clash between different international data protection and security regulations.
    • Highlighted specific risks like model inversion attacks and data localization requirements.
    • Introduced the Federated Data Sharing & Use Agreement (F-DSA), Governance as a Service (GaaS), and blockchain-based dynamic consent as key components of the proposed framework.

    Conclusions:

    • Federated Learning alone does not guarantee international compliance in healthcare AI.
    • A comprehensive Federated Governance Framework is essential to address legal, ethical, and technical challenges.
    • The proposed framework offers a blueprint for operationalizing international Learning Health Systems in a compliant and ethical manner.