Federated Learning, Privacy-Preserving Machine Learning, and Medical Diagnosis Systems across Distributed Networks

Abstract

This research explores a federated learning framework designed to address privacy concerns in medical diagnostics across geographically distributed hospitals. Traditional machine learning approaches require centralized data, often violating patient privacy and regulatory standards like HIPAA. We propose a privacy-preserving solution using federated learning with a hybrid CNN-LSTM architecture, allowing multiple institutions to collaboratively train models without sharing raw patient data. Using medical imaging datasets (pneumonia and skin cancer), we evaluate the performance, communication cost, and resilience to non-IID data distributions. Our results demonstrate that the federated model achieves near-centralized performance (94.2% pneumonia, 91.8% skin lesions) while preserving data privacy, suggesting strong potential for real-world clinical adoption.