Robust Healthcare AI Frameworks Mitigating Adversarial Attacks and Personal Data Stress Anxiety
Onyii Henry
*
University of the District of Columbia, 4200 Connecticut Ave NW, Washington, DC 20008, United States.
Christopher Ugbong Akeke
Howard University, 2400 Sixth Street NW, Washington, DC 20059-0001, USA.
Damilola Abidemi Akinwunmi
Glasgow Caledonian University, Cowcaddens Road, Glasgow, G4 0BA, Scotland, United Kingdom.
Cornelia Ifeoma Ejoh
University of the District of Columbia, 4200 Connecticut Ave NW, Washington, DC 20008, United States.
Akinde Michael Ogunmolu
Texas A&M University, 700 University Blvd, Kingsville, TX 78363, United States.
*Author to whom correspondence should be addressed.
Abstract
This study presents a hybrid federated learning and explainable artificial intelligence framework, termed RobustFL, designed to mitigate adversarial attacks in medical imaging while incorporating privacy-preserving mechanisms. Focusing on chest X-ray analysis, the research systematically examined adversarial vulnerabilities and data privacy challenges in distributed healthcare systems. A simulation-based methodology was implemented using a 500-sample dataset partitioned across multiple clients in a non-independent and identically distributed setting. The framework extends federated averaging by integrating projected gradient descent-based adversarial training, differential privacy stochastic gradient descent, and SHAP-based interpretability. Experimental results demonstrate that RobustFL maintains stable performance under adversarial conditions, achieving 70% accuracy against FGSM and PGD attacks, with an adversarial success rate of approximately 30–31%. A balanced privacy budget of ε = 1.0 produced an F1-score of 0.412, indicating a trade-off between privacy and model utility. Interpretability consistency, measured via SHAP outputs, served as a proxy for trust assessment, while privacy risk indicators were used to infer potential user concern. The framework provides a practical pathway toward secure, transparent, and privacy-aware healthcare AI systems.
Keywords: Federated learning, adversarial robustness, explainable AI, medical imaging, differential privacy