Federated Deep Learning for Predictive Healthcare: A Privacy-Preserving AI Framework on Cloud-Native Infrastructure

Abstract

Healthcare-related data is both sensitive and highly beneficial for developing accurate prediction models with prac- tical clinical impact. Given the potential threats to privacy in sharing these clinical datasets, this research proposes a feder- ated hybrid learning architecture as a Privacy-Preserving AI framework for Predictive Healthcare, offering a comprehensive solution for building secure and trustworthy predictive healthcare systems on cloud-native infrastructure. The Data Provenance and Governance Module traces the data to its origin, assesses its quality, detects privacy-hotspot attributes, and creates data- quality-aware determined charting rules that data-consuming services (e.g., predictive healthcare models) leverage to retrieve data samples. The Federated Model Training Pipeline Module builds prediction models on femtoclouds for Clinical Outcome Prediction, Early Warning and Risk Stratification, and Person- alized Medicine, minimizing the health data’s exposure to direct privacy attacks. In federated model training, Local Utility Models improve the quality of the predictive information exchanged among femtoclouds while mitigating the risk of differential at- tacks, and the model-training process preserves patients’ privacy against potential adversarial femtocloud nodes. The Federated Model Training Pipeline Module reduces the characteristics of the communication relation matrix and leverages these reduction patterns to discard the noisy and sensitive elements of the federated learning communication relation dataset.