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Federated Generative Frameworks for Adaptive Privacy Preservation in Healthcare Systems
Abstract
This chapter details an architecture for privacy-preserving interoperability to enable remote healthcare institutions to collaborate safely, supported by federated learning and generative modelling. It overcomes the direct sharing of sensitive medical records through adaptive privacy control algorithms for the realization of representative datasets. The proposed solution of joint local model training over heterogeneous data, statistically consistent synthetic data augmentation, and dynamic privacy governance at the data generation layer is designed to address three fundamental challenges: data heterogeneity, inference attacks, and legal boundaries between institutions. This chapter covers the operational modes, governance issues, and system components that allow real-world implementation, while addressing important ethical and technological issues such as model robustness, scalability, and synthetic data authenticity. The framework enables practical solutions that support large-scale medical analytics without privacy-violating compromise during collaborative healthcare workflows.
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