Search

Browse Subject Areas

For Authors

Submit a Proposal

Federated Intelligent System for Healthcare

A Practical Guide

Edited by S. Rakesh Kumar, N. Gayathri and Seifedine Kadry
Copyright: 2025   |   Expected Pub Date:2025/01/30
ISBN: 9781394271351  |  Hardcover  |  
310 pages

One Line Description
This practical guide gives valuable insights for integrating advanced
technologies in healthcare, empowering researchers to effectively navigate
and implement federated systems to enhance patient care.

Audience
Data scientists, IT, healthcare and business professionals working towards innovations in the healthcare sector. The book will be especially helpful to students and educators.

Description
Federated Intelligent Systems for Healthcare: A Practical Guide explores the integration of federated learning and intelligent systems within the healthcare domain. This volume provides an in-depth understanding of how federated systems enhance healthcare practices, detailing their principles, technologies, challenges, and opportunities. Additionally, this book addresses secure and privacy-preserving sharing of medical data, applications of artificial intelligence and machine learning in healthcare, and ethical considerations surrounding the adoption of these advanced technologies. With a focus on practical implementation and real-world use cases, Federated Intelligent Systems for Healthcare: A Practical Guide equips healthcare professionals, researchers, and technology experts with the knowledge needed to navigate the complexities of federated intelligent systems in healthcare and harness their potential to transform patient care and medical advancements.
Readers will find the book:
•Provides cutting-edge research from industry experts to unlock the future of healthcare with innovative insights that embrace federated intelligence and shape the future;
•Presents novel technologies and conceptual and visionary-based scenarios;
•Discusses real-world case studies and implementations that illustrate how federated intelligence is practically applied across various healthcare scenarios, from personalized diagnostics to population-level insights;
•Stands as a pioneer in the exploration of federated intelligent systems in healthcare.

Back to Top
Author / Editor Details
S. Rakesh Kumar, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Gandhi Institute of Technology and Management, Visakhapatnam, India. He has published four books and over 50 articles in international journals and conference proceedings. His research interests include artificial intelligence, machine learning, and big data applications.

N. Gayathri, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Gandhi Institute of Technology and Management, Visakhapatnam, India. She has published four books and over 50 articles in international journals and serves as a guest editor and reviewer for several journals of repute. Her research interests include big data analytics, Internet of Things, and machine learning.

Seifedine Kadry, PhD, is a professor in the Department of Applied Data Science at Noroff University and Lebanese American University. He serves as an ABET program evaluator, distinguished speaker of the Institute of Electrical and Electronics Engineers’ Computer Society, and a fellow of several other international societies. His research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics.

Back to Top

Table of Contents
Preface
1. Introduction to Federated Intelligent Systems in Healthcare
Naseem Ahmad
1.1 Introduction
1.2 Evolution and Principles of Federated Learning in Healthcare
1.3 Applications of Federated Learning in Healthcare
1.4 Challenges and Limitations of Federated Learning in Healthcare
1.5 Future Directions and Innovations in Federated Healthcare Systems
1.6 Conclusion
References
2. Federated Autonomous Deep Learning for Distributed Healthcare System
Rakesh Mohan Pujahari, Rijwan Khan and Satya Prakash Yadav
2.1 Introduction
2.2 Background
2.3 Use of Federated Learning
2.4 Smart and Efficient Healthcare Systems: Various Types of Federated Learning
2.5 Healthcare Integrated Learning in IoMT Apps
2.6 Federated Learning Based on Federated Mechanisms and Difficulties in Healthcare Applications
2.6.1 Data Security and Breach
2.6.2 Heterogeneity of Data
2.6.3 Compliance Regulatory Mechanism
2.6.4 Data Governance
2.6.5 Process of Communication Overhead
2.6.6 Model Selection and Aggregation
2.6.7 Annotation and Labeling of Data
2.6.8 Model Drift
2.6.9 Resource Constraints
2.6.10 Bias and Fairness
2.6.11 Interoperability
2.6.12 Engagement and Incentives Related to Patients
2.6.13 Scalability
2.6.14 Considerations Based on Ethics
2.7 Healthcare Issues and Their Solutions Related to Federated Learning
2.8 Directions for Future Use of Federated Learning in the Medical System
2.9 Conclusion
References
3. Intelligent Fusion: Federated Learning and Blockchain in Sustainable Healthcare 5.0
Pankaj Kumar Jadwal and Hemant Kumar Saini
3.1 Introduction
3.2 Distributed Data in Healthcare
3.2.1 FL Algorithms
3.2.2 Blockchain Approaches
3.2.3 Fusion of BC and FL
3.2.4 Framework/Architecture
3.3 IoHT Applications and Their Wideband Challenges
3.3.1 In Medical Units
3.3.2 Remote Healthcare from Homes
3.3.3 Patient-Generated Data
3.4 Tools
3.5 Case Studies
3.6 Conclusion
Future Directions
References
4. Foundations of Federated Intelligent Systems in Healthcare
Rachna Behl, Indu Kashyap and Neha Garg
4.1 Introduction
4.2 Core Concepts of Federated Learning
4.2.1 Federated Learning Training Process
4.2.2 Key Principles of Federated Learning
4.2.3 Comparing Traditional and Federated Learning: Data Management, Privacy, Scalability, and Performance
4.2.4 Applications of Federated Learning
4.3 FL in Healthcare
4.3.1 Need of FL in Healthcare
4.3.2 Types of FL for Healthcare
4.3.3 Role of Federated Learning in Healthcare
4.4 Federated Learning in Healthcare: Case Studies
4.5 Challenges and Ethical Consideration
4.6 Conclusion and Future Scope
References
5. Integrating Edge Devices and Internet of Medical Things in Modern Healthcare
Manoj Kumar Patra and Nandita Bhanja Chaudhuri
5.1 Introduction
5.1.1 Importance and Impact on Modern Healthcare
5.1.2 Historical Context and Evolution of Medical Technology
5.2 Edge Devices in Healthcare
5.2.1 Functionalities of Edge Devices in Patient Monitoring
5.2.2 Edge Device Applications in Healthcare
5.3 Internet of Medical Things (IoMT)
5.3.1 IoMT for Enhanced Healthcare Delivery
5.3.2 Integration of IoMT with Existing Healthcare Systems
5.4 Benefits of Integrating Edge Devices and IoMT
5.4.1 Accuracy and Efficiency in Diagnostics and Treatment
5.4.2 Reduction in Latency and Faster Decision-Making
5.4.3 Cost-Effectiveness and Resource Optimization in Healthcare
5.5 Key Technologies Enabling Integration
5.5.1 Edge Computing
5.5.2 Data Analytics and Machine Learning for Healthcare Insights
5.5.3 Communication Protocols and Standards
5.5.4 Cloud Computing for Data Storage and Processing
5.6 Applications and Use Cases of Edge Devices and IoMT
5.6.1 Remote Patient Monitoring and Telemedicine
5.6.2 Chronic Disease Management
5.6.3 Emergency Response Systems and Critical Care
5.6.4 Smart Hospitals and Healthcare Facilities
5.7 Challenges and Considerations
5.7.1 Data Privacy and Security Concerns in IoMT and Edge Devices
5.7.2 Interoperability and Integration with Existing Healthcare Infrastructure
5.7.3 Scalability and Network Reliability
5.7.4 Regulatory and Compliance Issues
5.8 Future Trends and Innovations
5.8.1 Advances in Edge Computing Technologies and Their Potential Impact
5.8.2 Emerging Applications of IoMT in Personalized Medicine
5.8.3 Integration with Artificial Intelligence and Predictive Analytics
5.8.4 Potential for Blockchain in Securing IoMT Data
5.9 Conclusion
References
6. Cloud Infrastructure and Federated Learning
Kanishka Gupta, Amit Aylani, Prakash Parmar and Deepak Hajoary
6.1 Foundations of the Future: Cloud Infrastructure Meets Federated Learning
6.1.1 Types of Cloud Deployments
6.1.2 Services of Cloud Computing
6.2 What is Federated Learning?
6.2.1 Mechanics of Federated Learning
6.3 The Essence of Collaboration: Federated Learning Unveiled
6.3.1 Concept and Working of Federated Learning
6.3.2 Types of Federated Learning
6.4 Harmonizing Cloud and Edge: The Integration Paradigm
6.4.1 Leveraging Cloud Resources for Federated Learning
6.4.2 Deployment of Federated Learning Models on the Cloud
6.4.3 How Federated Learning Models are Deployed on the Cloud
6.5 Real-World Applications of Federated Learning
6.6 Conclusion and Future Directions
References
7. Machine Learning and Artificial Intelligence Fundamentals for Federated Systems
N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha and Rajesh Sharma R.
7.1 Overview of Machine Learning and Artificial Intelligence
7.1.1 Definition of Machine Learning
7.1.2 Definition of Artificial Intelligence
7.1.3 Importance of ML and AI in Modern Technology
7.2 Key Concepts in Machine Learning
7.2.1 Data and Features
7.2.2 Algorithms and Models
7.2.3 Training and Testing
7.3 Fundamentals of Artificial Intelligence
7.3.1 Neural Networks
7.3.2 Deep Learning
7.3.3 Natural Language Processing (NLP)
7.3.4 Reinforcement Learning
7.4 Federated Learning
7.4.1 Definition and Importance
7.4.2 Architecture of Federated Learning Systems
7.4.3 Applications of Federated Learning
7.5 Challenges in Federated Learning
7.5.1 Data Heterogeneity
7.5.2 Communication Efficiency
7.5.3 Privacy and Security
7.5.4 System and Computational Constraints
7.6 Key Algorithms for Federated Learning
7.7 Model Aggregation and Optimization
7.7.1 Aggregation Techniques
7.7.2 Optimization Algorithms
7.8 Conclusion
References
8. Reconstructing Healthcare Foundations: Building Blocks of Federated Systems in Medical Technology
Blessing Takawira and David Pooe
Introduction
Historical Context and Evolution of Healthcare Systems
Fundamental Concepts of Federated Healthcare Systems
Technological Foundations
Building Blocks of Federated Healthcare Systems
Communication Protocols
Edge Devices and IoMT Integration
Privacy and Security Considerations
Systematic Literature Review Process
Solutions and Recommendations
Future Research Directions
Conclusion
References
Key Terms and Definitions
9. Federated Learning in Brain Tumor Segmentation in Medical Imaging
Jyoti Kataria and Supriya P. Panda
9.1 Introduction to Federated AI in Medical Imaging
9.1.1 Federated Learning Key Concepts
9.1.2 Overview of AI Techniques and Key Architectures Used in Segmentation
9.1.3 Importance of Accurate Segmentation in Diagnosis and Treatment
9.2 Traditional Segmentation Methods
9.2.1 Overview of Traditional Techniques
9.2.2 Advantages of Traditional Methods
9.2.3 Limitations of Traditional Methods
9.3 AI-Based Segmentation Methods
9.3.1 Convolutional Neural Networks (CNNs)
9.3.1.1 The Benefits of CNN-Based Segmentation
9.3.2 U-Net and its Variants
9.3.2.1 U-Net’s Advantages for Brain Tumor Segmentation
9.3.3 ResNet50
9.3.3.1 ResNet’s Advantages for Brain Tumor Segmentation
9.3.4 Benefits of Federated Learning in Brain Tumor Segmentation
9.3.5 Comparison of Brain Tumor Segmentation Methods
9.3.6 Case Studies by Different Institutions
9.4 Advantages and Challenges of AI-Based Methods
9.4.1 Advantages
9.4.2 Challenges
9.5 Federated Learning Workflow for Brain Tumor Segmentation
9.6 Notable Projects and Research
9.6.1 Federated Tumor Segmentation (FeTS) Initiative
9.6.2 Federated Learning for Healthcare (FL4HC)
9.6.3 AI for Health by NVIDIA Clara’s
9.6.4 The Role of FL in BraTS
9.6.5 Collaborative Research with Hospitals and Universities
9.6.6 OpenFL by Intel
9.6.7 Google Health’s Federated Learning Projects
9.7 Conclusion
9.8 Future Scope
References
10. Disease Prediction and Early Diagnosis Using Federated Models
Vibha Tiwari, B. K. Mishra, Nitya Hari Das, Balwinder Singh and Harmandeep Kaur
10.1 Introduction
10.1.1 FL in Healthcare
10.2 Related Works
10.2.1 Machine Learning (Deep Learning)
10.2.2 Horizontal FL
10.2.3 Vertical FL
10.2.4 Federated Transfer Learning
10.3 Proposed Method
10.3.1 Local Machine or Local Hospital Selection for Collecting Dataset
10.3.2 Upload to the Server
10.3.3 Client Computation
10.3.4 Sum-Up All the Devices Dataset
10.3.5 Update Model
10.4 Result Discussion
10.4.1 Dataset Description
10.5 Conclusion & Future Work
References
11. Navigating Bias and Ensuring Fairness in Federated Learning: An In-Depth Exploration of Data Distribution, IID, and Non-IID Challenges
Vajratiya Vajrobol, Nitisha Aggarwal, Pushkar Baranwal, Geetika Jain Saxena, Amit Pundir and Sanjeev Singh
11.1 Introduction to Federated Learning and Data Distribution
11.2 Understanding Data Bias in Federated Learning
11.3 Implications of Data Bias in Federated Learning
11.4 Fairness in Federated Learning
11.5 Approaches to Address Data Bias and Ensure Fairness
11.6 Evaluating and Mitigating Bias in Federated Learning
11.7 Case Studies and Examples
11.8 Ethical Considerations and Responsible AI
11.9 Future Directions and Research Challenges
11.10 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page