Federated Transfer Learning Models for Privacy-Preserving Predictive Diagnostics in Heterogeneous Healthcare Systems Using Distributed Cloud Computing Frameworks

Gerald de Basto D

Authors

Gerald de Basto D Software Consultant, USA Author

Keywords:

Federated learning, transfer learning, privacy preservation, healthcare diagnostics, cloud computing, model generalization, heterogeneous systems

Abstract

The growing need for predictive diagnostics in healthcare demands both high-performing models and strict privacy assurances. This study presents a federated transfer learning (FTL) approach deployed within a distributed cloud computing framework to facilitate diagnostic predictions without centralized data collection. Addressing the heterogeneity of healthcare data across institutions, our method ensures privacy-preserving collaboration while enhancing model generalizability. We evaluate this approach on synthetic and real-world datasets (MIMIC-III and COVIDx), demonstrating its efficacy in maintaining diagnostic accuracy and minimizing data exposure.

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