Ai-Based Federated Learning Framework for Confidential Healthcare Management

Abstract

Artificial Intelligence (AI) is increasingly used in healthcare to support diagnosis, personalized treatment, predictive analytics, and continuous patient monitoring. At the same time, regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) place strict limits on how patient data can be used, and gathering personal health records into one place raises serious ethical concerns. Federated Learning (FL) offers a way around this: hospitals and medical devices can jointly train a shared model without ever moving patient data from where it originated. This paper presents a Federated Learning Optimization Framework (MFLOF) built around the Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) control loop. Rather than picking clients at random, MFLOF adapts client selection to changing operational conditions such as unreliable devices, shifting data distributions, varying privacy requirements, and limited bandwidth. On the MIMIC-III and ChestX-ray14 healthcare datasets, the framework holds onto strong predictive accuracy while cutting communication cost and strengthening privacy protection.