Advancing Healthcare Data Integration through Federated Interoperability Models and Privacy-Preserving Machine Learning Frameworks

Natalia Varga

Authors

Natalia Varga Slovakia Author

Keywords:

Healthcare data integration, Federated learning, Interoperability, Privacy-preserving machine learning, , Federated interoperability, Secure computation, Data privacy, Healthcare informatics

Abstract

The healthcare sector continues to face major challenges in data interoperability, especially in environments requiring strict privacy compliance. Centralized data architectures are increasingly inadequate, leading to fragmented care delivery and stunted innovation. This paper explores a federated approach to healthcare data integration, combining interoperable models with privacy-preserving machine learning (PPML). We investigate the application of federated learning (FL) as a mechanism to enable decentralized data collaboration without compromising patient confidentiality. Drawing on recent developments, this study outlines a framework that balances security, efficiency, and real-world adaptability.

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