Federated Quantum Convolutional Neural Networks in Healthcare

Enhancing Privacy and Efficiency
Erscheinungsjahr 2027
ISBN: 978-0-443-51136-3
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm

Federated Quantum Convolutional Neural Networks in Healthcare: Enhancing Privacy and Efficiency explores the innovative intersection of quantum computing and federated learning in the context of healthcare. As medical data grows exponentially and privacy concerns intensify, this book addresses the urgent need for secure, scalable AI solutions that enable collaborative data analysis without compromising patient confidentiality. It offers a detailed examination of the theoretical foundations of quantum machine learning and federated systems, complemented by practical case studies spanning medical imaging, genomics, and disease diagnosis. The content delves into architecture design, security protocols, and optimization techniques, equipping researchers and practitioners with the tools to develop privacy-preserving AI models tailored for medical applications. The book also discusses regulatory considerations, deployment challenges, and future research directions, positioning itself as a vital resource for advancing healthcare AI. Its comprehensive approach bridges fundamental science and real-world implementation, empowering healthcare professionals, data scientists, and policy makers to harness next-generation quantum federated systems for improved patient outcomes and data security. This resource is essential for those seeking to implement cutting-edge AI technologies in healthcare, ensuring secure, efficient, and collaborative medical data analysis.

Autoren/Hrsg.

Kumar, Abhishek

Batta, Priya

Ramírez, Reyes Juárez

Ananth, J.P.

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

1. Introduction to Federated Quantum Learning
2. Foundations of Quantum Computing and Federated Learning
3. Challenges in Healthcare Data Privacy and Security
4. Principles and Architecture of Quantum Convolutional Neural Networks (QCNNs)
5. Quantum Machine Learning for Healthcare
6. Federated Quantum Convolutional Neural Networks (FedQCNNs)
7. Design, Implementation, and Deployment of FedQCNNs
8. Secure Communication and Aggregation Protocols
9. Privacy-Preserving Methods for Healthcare Data
10. Efficiency, Scalability, and Optimization Techniques
11. Evaluation and Performance on Real-World Datasets
12. Medical Image Analysis and Genomics Applications
13. Case Studies in Real-World Healthcare Scenarios
14. Quantum Noise, Error Correction, and Practical Limitations
15. Integration with Classical Federated Learning Models
16. Collaborative Frameworks and Institutional Deployments
17. Regulatory and Legal Frameworks for FedQCNNs
18. Future Directions and Impact on Healthcare Innovation

Über Autor(innen)

Ananth, J.P.
Dr J. P. Ananth is Professor of Computer Science & Engineering and Director, IQAC, at Dayananda Sagar University, Bengaluru. He holds a B.E. and M.E. in Computer Science from MS University and a PhD from Sathyabama University, Chennai. With 23 years of academic experience, he has previously served as Professor and Dean–IQAC, Coimbatore, where he led NAAC Cycle II to an A++ accreditation in 2024. His research interests include Computer Vision, Pattern Recognition, AI, and Data Analytics, supported by DST-TIDE funding. He has published impactful research in reputed journals such as Expert Systems with Applications, has supervised six PhD scholars, and reviews for leading international journals and conferences.

Kumar, Abhishek
Abhishek Kumar is Assistant Director and Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He holds a Ph.D. in Computer Science from the University of Madras and is currently a Post-Doctoral Fellow with the Ingenium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He received his M.Tech in Computer Science and Engineering and B.Tech in Information Technology from Rajasthan Technical University, Kota, India. He has over thirteen years of academic teaching experience. His research interests include artificial intelligence, computer vision, image processing, data mining, machine learning, and renewable energy systems. He has authored and edited several books with leading international publishers and serves as a reviewer for reputed journals.

Ramírez, Reyes Juárez
Dr Reyes Juárez Ramírez is a Full Professor of Computer Science at the Autonomous University of Baja California, Tijuana, Mexico. He currently serves as President of the Mexican Network of Software Engineering and is a Level 2 member of Mexico’s National System of Researchers. He leads several industry-linked research projects and specializes in applying data science to software engineering. His work focuses on uncertainty in agile methodologies, quality enhancement in Scrum, user-centered design, adaptive interfaces, and emerging research in quantum computing. He has also served as General Chair for the National and International Conference on Software Engineering Research and Innovation.

Batta, Priya
Priya Batta is an Associate Professor in the Department of Computer Science and Engineering at Chandigarh University, Mohali, India. She holds a Ph.D. in Computer Science and Engineering from Chandigarh University, an M.Tech in Computer Science from Punjabi University, Patiala, a B.Tech in Information Technology from Chandigarh Engineering College, Landran, Punjab, and a Diploma in Information Technology from Thapar Polytechnic College, Patiala. She has over ten years of academic teaching experience. Her research interests include artificial intelligence, blockchain, and the Internet of Things. She has published in reputed national and international journals and conferences and has edited several books with leading academic publishers.