Co-Training-Based Personalized Federated Learning With Generative Adversarial Networks for Enhanced Mobile Smart Healthcare Diagnosis

Abstract

The widespread implementation of Artificial Itelligence (AI) has led to significant advancements in disease diagnosis. Personalized Federated Learning (FL) trains models tailored to each patient’s needs but often overlooks model architecture heterogeneity. We propose a novel Co-training-based personalized FL with Generative Adversarial Networks (GANs) for Smart Healthcare Diagnosis (CFG-SHD). This approach allows privacy-preserving participation in FL by enabling patients to keep their model architectures and parameters private. Key contributions include integrating co-training into FL for leveraging multiple data views and using GANs to generate synthetic data, ensuring data privacy. By addressing model architecture heterogeneity our approach offers a robust solution for personalized healthcare diagnostics, aligning with the diverse needs of modern healthcare systems and advancing patient-centric AI applications. CFG-SHD enhances personalized diagnosis accuracy, achieving 97.16%, 98.04%, and 97.88% on the PAD-UFES-20, HAM10000, and PH2 datasets, respectively.