Federated AI and Cloud Computing for Secure Healthcare Data Collaboration
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Abstract
To enable privacy-preserving data collaboration in healthcare, this paper examines the integration of federated AI and cloud-based data-sharing approaches. These complementary paradigms overcome common challenges associated with isolating sensitive patient information and enable timely responses to real-world health crises. Specific attention is given to secure methods and standards for sharing data, information, and models, as well as end-to-end architectures that orchestrate data flows between federated nodes and cloud services. Healthcare is cited as one of the least interoperable sectors, where access to critical data is hampered by strict governance rules and ethical considerations. Regulatory frameworks such as HIPAA and GDPR protect sensitive patient information but also prevent data sharing for good causes. An alternative consent framework based on individual-level privacy risk and general adoption at the population level enables federated analysis of pooled data without direct data sharing.
Federated learning (FL) allows artificial intelligence (AI) analysts to create AI models without having to centralize sensitive patient data in a single location. A risk-aware mechanism for multi-institutional federated AI applied to real-world cancer genomics and public health data is developed, proving that adopting federated AI would not create a larger privacy risk when compared to the traditional approach of sharing the data among institutions.
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