Advancing Healthcare Analytics: The Role of Federated Learning and Explainability in Ensuring Data Privacy and Security

Dear Colleagues,

Federated learning and explainability are two emerging technologies that have the potential to revolutionize healthcare analytics by enabling secure and privacy-preserving collaboration between multiple healthcare institutions. This Special Issue aims to explore the role of these technologies in advancing healthcare analytics and ensuring data privacy and security. We welcome original research and innovative ideas on how federated learning can be used to collaborate effectively and efficiently in a distributed healthcare environment. We also invite submissions that examine the potential of explainability in healthcare analytics, particularly in the areas of transparency, fairness, and accuracy. The goal is to provide a platform for researchers, clinicians, and practitioners to share their insights and experiences on how these technologies can be harnessed to improve healthcare management and better livelihood, while safeguarding patient privacy and data security. We invite high-quality submissions that demonstrate state-of-the-art applications of federated learning and explainability in healthcare analytics and explore new research directions for advancing these technologies in the field of healthcare management engineering.

Topics:

The topics of interest include but are not limited to Advancing Healthcare Analytics: The Role of Federated Learning and Explainability in Ensuring Data Privacy and Security in the following research scope.

1. Federated learning techniques for healthcare data management;
2. Explainability in healthcare analytics: methods and applications;
3. Federated-learning-based predictive modeling for personalized medicine;
4. Privacy-preserving machine learning for healthcare analytics;
5. Federated learning for medical imaging analysis;
6. Explainable deep learning for healthcare data analysis;
7. Secure and privacy-preserving federated learning for healthcare fraud detection;
8. Federated-learning-based disease surveillance and outbreak prediction;
9. Explainability and accountability in AI-assisted diagnosis and treatment planning;
10. Federated-learning-based clinical trial design and analysis;
11. Explainable AI for clinical decision support systems;
12. Federated learning and explainability in health information exchange and interoperability;
13. Ethical considerations in federated learning and explainability for healthcare management;
14. Challenges and opportunities in deploying federated learning and explainability in healthcare settings;
15. Real-world applications and case studies of federated learning and explainability in healthcare analytics.

Technical Program Committee Member:
Name: Prof. Dr. Satheesh Abimannan
Email: [email protected]
Affiliation: School Engineering and Technology, Amity University, Mumbai 410206, India
Research Interests: deep learning; federated learning; cybersecurity; data analytics

Dr. John Ayeelyan
Prof. Dr. George A. Tsihrintzis
Guest Editors

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• federated learning
• explainability
• healthcare analytics
• data privacy and security