1st Edition
Federated Deep Learning for Healthcare A Practical Guide with Challenges and Opportunities
This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.
Features:
- Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications.
- Investigates privacy-preserving methods with emphasis on data security and privacy.
- Discusses healthcare scaling and resource efficiency considerations.
- Examines methods for sharing information among various healthcare organizations while retaining model performance.
This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
1. Revolutionizing Healthcare through Federated Learning: A Secure and Collaborative Approach
Amrina Rahman, Md. Mushfiqur Rahman, Farhana Yasmin
2. Revolutionizing Healthcare: Unleashing the Power of Digital Health
Renu Vij
3. Federated Deep Learning Systems in Healthcare
Ashraful Reza Tanjil, Fahim Mohammad Adud Bhuiyan, Mohammad Abu Tareq Rony, Kamanashis Biswas
4. Applications of Federated Deep Learning Models in Healthcare Era
Monika Sethi, Jyoti Snehi, Manish Snehi, Aadrit Aggarwal
5. Machine Learning for Healthcare- Review and future Aspects
Aadrita Nandy, Jyoti Choudhary, Joanne Fredrick, T S Zacharia, Tom K Joseph, Veerpal Kaur
6. Federated Multi Task Learning to Solve Various Healthcare Challenges
Seema Pahwa, Amandeep Kaur
7. Smart System for Development of Cognitive Skills Using Machine Learning
Rashmi Aggarwal, Uday Devgan, Sandhir Sharma, Tanvi Verma, Aadrit Aggarwal
8. Patient-Driven Federated Learning (PD-FL) – An Overview
A.Menaka Devi, Ms.V.Megala
9. An Explainable and Comprehensive Federated Deep Learning in Practical Applications: Real World Benefits and Systematic Analysis Across Diverse Domains
Khalid Aziz, Sakshi Dua, Prabal Gupta
10. Federated deep learning system for application of health care of pandemic situation
Vandana, Chetna Kaushal
11. The integration of federated deep learning with Internet of Things in the healthcare sector
Hirak Mondal, Md. Mehedi Hassan, Anindya Nag, Anupam Kumar Bairagi
12. FireEye: An IoT-Based Fire Alarm and Detection System for Enhanced Safety
Md. Moynul Islam, Nahida Fatme, Md AL Mahbub Hossain, Muhammad Fiazul Haque
13. Safeguarding Data Privacy and Security in Federated Learning Systems
Wasswa Shafik, Kassim Kalinaki, Khairul Eahsun Fahim, Mumin Adam
14. Computer Vision Based Fruit Diseases Detection System using Deep Learning
P. Dhiman, S. Wadhwa, A.Kaur
15. Tailoring Medicine Through Personalized Healthcare Solutions
Tejinder Kaur, Madhav Aggarwal, Krish Wason, Pragati Duggal
16. FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare: Health Monitoring and Healthcare Informatics Lensing Challenges and Future Directions
Bhupinder Singh, Christian Kaunert
17. From Scarce to Abundant: Enhancing Learning with Federated Transfer Techniques
Rezuana Haque, Md. Mehedi Hassan, Sheikh Mohammed Shariful Islam
18. Federated Learning-Based AI Approaches for Predicting Stroke Disease
Satyajit Roy, Fariha Ferdous Mim, Md. Mehedi Hassan, Sheikh Mohammed Shariful Islam
Biography
Dr. Amandeep Kaur currently holds the position of a professor at the Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab. She earned her doctorate degree from I. K. Gujral Punjab Technical University, Jalandhar. Dr. Kaur’s academic achievements include receiving both her M.Tech (Computer Science and Engineering) and B.Tech (Computer Science and Engineering) degrees with distinction. Additionally, she has successfully qualified UGC‑NET in Computer Science. Dr. Kaur boasts an extensive research portfolio, with approximately 100 publications in renowned international journals and fully refereed international conferences. She has accumulated 24 years of valuable experience in her field and has filed and published more than 107 patents. Dr. Kaur has played a significant role in mentoring the academic growth of over 30 Ph.D. and PG students. Her primary research areas encompass medical informatics, machine learning, IoT (Internet of Things), artificial intelligence, and cloud computing. Notably, Dr. Kaur has been recognized for her exceptional contributions, winning the Excellence Award in the "Filing Patent" category for three consecutive years (2021, 2022, and 2023) and the Best Ph.D. Supervision Award in 2023. Furthermore, she has achieved recognition on a global scale, as she is included in Stanford University’s prestigious list of the top 2% most influential scientists among Indian researchers. This underscores her significance in the field of computer science and research.
Dr. Chetna Kaushal works as an assistant professor in Chitkara University, Punjab. She has done a Ph.D. in Computer Science and Engineering from Chitkara University, Punjab, M.Tech in Computer Science and Engineering from DAV University, Punjab, and B.Tech in Information Technology from Punjab Technical University. Her areas of expertise are machine learning, soft computing, pattern recognition, image processing, and artificial intelligence. She has around ten years of experience in research, training, and academics. She has published numerous research papers in various international/national journals, books, and conferences. She has filed and published more than 50 patents to her name. She is a reviewer of many prestigious journals. Dr. Chetna Kaushal is an exceptionally motivated and talented researcher deeply dedicated to advancing human health and well‑being through pioneering scientific investigations. Her remarkable achievements thus far are a testament to her capabilities, and her potential for making significant future contributions to her field is undeniably bright.
Md. Mehedi Hassan is a dedicated young researcher, holding a B.Sc. Engineering degree in computer science and engineering from 2022 and currently pursuing his M.Sc. Engineering degree at Khulna University, Bangladesh. His remarkable aptitude for research has propelled him to excel in biomedical engineering, data science, and expert systems, earning him recognition as a respected leader in these fields. He is the founder and CEO of the Virtual BD IT Firm and the lab head of the VRD Research Laboratory in Bangladesh. With over three filed patents, three of which have been granted, Mehedi is not only an innovative thinker but also a practical problem solver. He also serves as a reviewer for prestigious journals, further underscoring his influence in the scientific community. Mehedi’s research interests encompass a broad spectrum, ranging from human brain imaging, neuroscience, machine learning, and artificial intelligence to software engineering. Driven by his notable accomplishments and promising potential, Mehedi remains dedicated to leveraging cutting‑edge scientific research to enhance human health and well‑being.
Dr. Si Thu Aung received his B.E. from Technological University, Myanmar, in 2014, the Master of Engineering in Electronics from Mandalay Technological University, Myanmar, in 2017, and a Ph.D. in Biomedical Engineering from the Faculty of Engineering, Mahidol University, Thailand, in 2021. Previously, he worked as a post-doctoral researcher at the Rail and Modern Transports Research Center under the National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand. Now, he is working as a post-doctoral research associate at the Department of Mathematics at the State University of New York, Buffalo. His current research interests include biomedical signal processing, digital image processing, machine learning, and deep learning.
