1st Edition

Federated Deep Learning for Healthcare A Practical Guide with Challenges and Opportunities

    312 Pages 44 B/W Illustrations
    by CRC Press

    This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of 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 like 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.

    • 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


    Amandeep Kaur currently holds the position of a Professor at the Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab. Her primary research areas encompass medical informatics, machine learning, IoT (Internet of Things), artificial intelligence, and cloud computing. 

    Chetna Kaushal is working as an Assistant Professor in Chitkara University, Punjab. She is PhD in CSE from Chitkara University, Punjab, M.Tech in CSE from DAV University, Punjab and B.Tech in IT from Punjab Technical University. Her areas of expertise are Machine learning, Soft Computing, Pattern Recognition, Image processing, and Artificial Intelligence.

    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. Mehedi's research interests encompass a broad spectrum, ranging from human brain imaging, neuroscience, machine learning, and artificial intelligence to software engineering.

    Si Thu Aung received the B.E. degree from Technological University, Myanmar, in 2014, the Master of Engineering in Electronics from Mandalay Technological University, Myanmar, in 2017, and the Ph.D. in Biomedical Engineering from the Faculty of Engineering, Mahidol University, Thailand, in 2021. His current research interests include biomedical signal processing, digital image processing, machine learning, and deep learning.