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.

• Preface xiii1 Introduction to Federated Intelligent Systems in Healthcare 1Naseem Ahmad1.1 Introduction 21.2 Evolution and Principles of Federated Learning in Healthcare 41.3 Applications of Federated Learning in Healthcare 61.4 Challenges and Limitations of Federated Learning in Healthcare 111.5 Future Directions and Innovations in Federated Healthcare Systems 151.6 Conclusion 20References 212 Federated Autonomous Deep Learning for Distributed Healthcare System 25Rakesh Mohan Pujahari, Rijwan Khan and Satya Prakash Yadav2.1 Introduction 262.2 Background 272.3 Use of Federated Learning 282.4 Smart and Efficient Healthcare Systems: Various Types of Federated Learning 322.5 Healthcare Integrated Learning in IoMT Apps 332.6 Federated Learning Based on Federated Mechanisms and Difficulties in Healthcare Applications 352.6.1 Data Security and Breach 352.6.2 Heterogeneity of Data 382.6.3 Compliance Regulatory Mechanism 392.6.4 Data Governance 392.6.5 Process of Communication Overhead 402.6.6 Model Selection and Aggregation 422.6.7 Annotation and Labeling of Data 432.6.8 Model Drift 452.6.9 Resource Constraints 462.6.10 Bias and Fairness 472.6.11 Interoperability 482.6.12 Engagement and Incentives Related to Patients 492.6.13 Scalability 502.6.14 Considerations Based on Ethics 512.7 Healthcare Issues and Their Solutions Related to Federated Learning 522.8 Directions for Future Use of Federated Learning in the Medical System 542.9 Conclusion 55References 563 Intelligent Fusion: Federated Learning and Blockchain in Sustainable Healthcare 5.0 61Pankaj Kumar Jadwal and Hemant Kumar Saini3.1 Introduction 623.2 Distributed Data in Healthcare 643.2.1 FL Algorithms 643.2.2 Blockchain Approaches 663.2.3 Fusion of BC and FL 663.2.4 Framework/Architecture 673.3 IoHT Applications and Their Wideband Challenges 713.3.1 In Medical Units 723.3.2 Remote Healthcare from Homes 723.3.3 Patient-Generated Data 733.4 Tools 733.5 Case Studies 743.6 Conclusion 76Future Directions 76References 774 Foundations of Federated Intelligent Systems in Healthcare 81Rachna Behl, Indu Kashyap and Neha Garg4.1 Introduction 824.2 Core Concepts of Federated Learning 834.2.1 Federated Learning Training Process 834.2.2 Key Principles of Federated Learning 854.2.3 Comparing Traditional and Federated Learning: Data Management, Privacy, Scalability, and Performance 864.2.4 Applications of Federated Learning 884.3 FL in Healthcare 884.3.1 Need of FL in Healthcare 884.3.2 Types of FL for Healthcare 894.3.3 Role of Federated Learning in Healthcare 904.4 Federated Learning in Healthcare: Case Studies 924.5 Challenges and Ethical Consideration 944.6 Conclusion and Future Scope 96References 975 Integrating Edge Devices and Internet of Medical Things in Modern Healthcare 101Manoj Kumar Patra and Nandita Bhanja Chaudhuri5.1 Introduction 1025.1.1 Importance and Impact on Modern Healthcare 1025.1.2 Historical Context and Evolution of Medical Technology 1035.2 Edge Devices in Healthcare 1035.2.1 Functionalities of Edge Devices in Patient Monitoring 1045.2.2 Edge Device Applications in Healthcare 1045.3 Internet of Medical Things (IoMT) 1055.3.1 IoMT for Enhanced Healthcare Delivery 1065.3.2 Integration of IoMT with Existing Healthcare Systems 1075.4 Benefits of Integrating Edge Devices and IoMT 1085.4.1 Accuracy and Efficiency in Diagnostics and Treatment 1085.4.2 Reduction in Latency and Faster Decision-Making 1095.4.3 Cost-Effectiveness and Resource Optimization in Healthcare 1095.5 Key Technologies Enabling Integration 1105.5.1 Edge Computing 1105.5.2 Data Analytics and Machine Learning for Healthcare Insights 1115.5.3 Communication Protocols and Standards 1115.5.4 Cloud Computing for Data Storage and Processing 1125.6 Applications and Use Cases of Edge Devices and IoMT 1135.6.1 Remote Patient Monitoring and Telemedicine 1135.6.2 Chronic Disease Management 1145.6.3 Emergency Response Systems and Critical Care 1145.6.4 Smart Hospitals and Healthcare Facilities 1155.7 Challenges and Considerations 1165.7.1 Data Privacy and Security Concerns in IoMT and Edge Devices 1165.7.2 Interoperability and Integration with Existing Healthcare Infrastructure 1175.7.3 Scalability and Network Reliability 1175.7.4 Regulatory and Compliance Issues 1185.8 Future Trends and Innovations 1195.8.1 Advances in Edge Computing Technologies and Their Potential Impact 1195.8.2 Emerging Applications of IoMT in Personalized Medicine 1205.8.3 Integration with Artificial Intelligence and Predictive Analytics 1215.8.4 Potential for Blockchain in Securing IoMT Data 1215.9 Conclusion 122References 1236 Cloud Infrastructure and Federated Learning 127Kanishka Gupta, Amit Aylani, Prakash Parmar and Deepak Hajoary6.1 Foundations of the Future: Cloud Infrastructure Meets Federated Learning 1286.1.1 Types of Cloud Deployments 1296.1.2 Services of Cloud Computing 1306.2 What is Federated Learning? 1336.2.1 Mechanics of Federated Learning 1346.3 The Essence of Collaboration: Federated Learning Unveiled 1366.3.1 Concept and Working of Federated Learning 1366.3.2 Types of Federated Learning 1386.4 Harmonizing Cloud and Edge: The Integration Paradigm 1406.4.1 Leveraging Cloud Resources for Federated Learning 1406.4.2 Deployment of Federated Learning Models on the Cloud 1456.4.3 How Federated Learning Models are Deployed on the Cloud 1466.5 Real-World Applications of Federated Learning 1496.6 Conclusion and Future Directions 150References 1517 Machine Learning and Artificial Intelligence Fundamentals for Federated Systems 153N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha and Rajesh Sharma R.7.1 Overview of Machine Learning and Artificial Intelligence 1547.1.1 Definition of Machine Learning 1547.1.2 Definition of Artificial Intelligence 1547.1.3 Importance of ML and AI in Modern Technology 1557.2 Key Concepts in Machine Learning 1557.2.1 Data and Features 1557.2.2 Algorithms and Models 1567.2.3 Training and Testing 1577.3 Fundamentals of Artificial Intelligence 1577.3.1 Neural Networks 1577.3.2 Deep Learning 1577.3.3 Natural Language Processing (NLP) 1587.3.4 Reinforcement Learning 1587.4 Federated Learning 1587.4.1 Definition and Importance 1587.4.2 Architecture of Federated Learning Systems 1597.4.3 Applications of Federated Learning 1597.5 Challenges in Federated Learning 1617.5.1 Data Heterogeneity 1617.5.2 Communication Efficiency 1617.5.3 Privacy and Security 1617.5.4 System and Computational Constraints 1627.6 Key Algorithms for Federated Learning 1627.7 Model Aggregation and Optimization 1647.7.1 Aggregation Techniques 1647.7.2 Optimization Algorithms 1647.8 Conclusion 166References 1668 Reconstructing Healthcare Foundations: Building Blocks of Federated Systems in Medical Technology 171Blessing Takawira and David PooeIntroduction 172Historical Context and Evolution of Healthcare Systems 174Fundamental Concepts of Federated Healthcare Systems 176Technological Foundations 179Building Blocks of Federated Healthcare Systems 180Communication Protocols 181Edge Devices and IoMT Integration 183Privacy and Security Considerations 185Systematic Literature Review Process 187Solutions and Recommendations 188Future Research Directions 191Conclusion 192References 193Key Terms and Definitions 1999 Federated Learning in Brain Tumor Segmentation in Medical Imaging 201Jyoti Kataria and Supriya P. Panda9.1 Introduction to Federated AI in Medical Imaging 2029.1.1 Federated Learning Key Concepts 2039.1.2 Overview of AI Techniques and Key Architectures Used in Segmentation 2059.1.3 Importance of Accurate Segmentation in Diagnosis and Treatment 2069.2 Traditional Segmentation Methods 2069.2.1 Overview of Traditional Techniques 2079.2.2 Advantages of Traditional Methods 2099.2.3 Limitations of Traditional Methods 2099.3 AI-Based Segmentation Methods 2109.3.1 Convolutional Neural Networks (CNNs) 2109.3.1.1 The Benefits of CNN-Based Segmentation 2119.3.2 U-Net and its Variants 2129.3.2.1 U-Net’s Advantages for Brain Tumor Segmentation 2139.3.3 ResNet 50 2149.3.3.1 ResNet’s Advantages for Brain Tumor Segmentation 2159.3.4 Benefits of Federated Learning in Brain Tumor Segmentation 2169.3.5 Comparison of Brain Tumor Segmentation Methods 2189.3.6 Case Studies by Different Institutions 2219.4 Advantages and Challenges of AI-Based Methods 2239.4.1 Advantages 2239.4.2 Challenges 2249.5 Federated Learning Workflow for Brain Tumor Segmentation 2259.6 Notable Projects and Research 2279.6.1 Federated Tumor Segmentation (FeTS) Initiative 2279.6.2 Federated Learning for Healthcare (FL4HC) 2279.6.3 AI for Health by NVIDIA Clara’s 2279.6.4 The Role of FL in BraTS 2289.6.5 Collaborative Research with Hospitals and Universities 2289.6.6 OpenFL by Intel 2289.6.7 Google Health’s Federated Learning Projects 2289.7 Conclusion 2299.8 Future Scope 229References 23010 Disease Prediction and Early Diagnosis Using Federated Models 233Vibha Tiwari, B. K. Mishra, Nitya Hari Das, Balwinder Singh and Harmandeep Kaur10.1 Introduction 23410.1.1 FL in Healthcare 23410.2 Related Works 23610.2.1 Machine Learning (Deep Learning) 23610.2.2 Horizontal FL 23810.2.3 Vertical FL 23810.2.4 Federated Transfer Learning 24010.3 Proposed Method 24110.3.1 Local Machine or Local Hospital Selection for Collecting Dataset 24210.3.2 Upload to the Server 24210.3.3 Client Computation 24210.3.4 Sum-Up All the Devices Dataset 24210.3.5 Update Model 24310.4 Result Discussion 24310.4.1 Dataset Description 24410.5 Conclusion & Future Work 248References 24911 Navigating Bias and Ensuring Fairness in Federated Learning: An In-Depth Exploration of Data Distribution, IID, and Non-IID Challenges 253Vajratiya Vajrobol, Nitisha Aggarwal, Pushkar Baranwal, Geetika Jain Saxena, Amit Pundir and Sanjeev Singh11.1 Introduction to Federated Learning and Data Distribution 25411.2 Understanding Data Bias in Federated Learning 26111.3 Implications of Data Bias in Federated Learning 26311.4 Fairness in Federated Learning 26511.5 Approaches to Address Data Bias and Ensure Fairness 26611.6 Evaluating and Mitigating Bias in Federated Learning 26911.7 Case Studies and Examples 27511.8 Ethical Considerations and Responsible AI 28211.9 Future Directions and Research Challenges 28311.10 Conclusion 284References 285Index 293

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.

• Preface xiii1 Introduction to Federated Intelligent Systems in Healthcare 1Naseem Ahmad1.1 Introduction 21.2 Evolution and Principles of Federated Learning in Healthcare 41.3 Applications of Federated Learning in Healthcare 61.4 Challenges and Limitations of Federated Learning in Healthcare 111.5 Future Directions and Innovations in Federated Healthcare Systems 151.6 Conclusion 20References 212 Federated Autonomous Deep Learning for Distributed Healthcare System 25Rakesh Mohan Pujahari, Rijwan Khan and Satya Prakash Yadav2.1 Introduction 262.2 Background 272.3 Use of Federated Learning 282.4 Smart and Efficient Healthcare Systems: Various Types of Federated Learning 322.5 Healthcare Integrated Learning in IoMT Apps 332.6 Federated Learning Based on Federated Mechanisms and Difficulties in Healthcare Applications 352.6.1 Data Security and Breach 352.6.2 Heterogeneity of Data 382.6.3 Compliance Regulatory Mechanism 392.6.4 Data Governance 392.6.5 Process of Communication Overhead 402.6.6 Model Selection and Aggregation 422.6.7 Annotation and Labeling of Data 432.6.8 Model Drift 452.6.9 Resource Constraints 462.6.10 Bias and Fairness 472.6.11 Interoperability 482.6.12 Engagement and Incentives Related to Patients 492.6.13 Scalability 502.6.14 Considerations Based on Ethics 512.7 Healthcare Issues and Their Solutions Related to Federated Learning 522.8 Directions for Future Use of Federated Learning in the Medical System 542.9 Conclusion 55References 563 Intelligent Fusion: Federated Learning and Blockchain in Sustainable Healthcare 5.0 61Pankaj Kumar Jadwal and Hemant Kumar Saini3.1 Introduction 623.2 Distributed Data in Healthcare 643.2.1 FL Algorithms 643.2.2 Blockchain Approaches 663.2.3 Fusion of BC and FL 663.2.4 Framework/Architecture 673.3 IoHT Applications and Their Wideband Challenges 713.3.1 In Medical Units 723.3.2 Remote Healthcare from Homes 723.3.3 Patient-Generated Data 733.4 Tools 733.5 Case Studies 743.6 Conclusion 76Future Directions 76References 774 Foundations of Federated Intelligent Systems in Healthcare 81Rachna Behl, Indu Kashyap and Neha Garg4.1 Introduction 824.2 Core Concepts of Federated Learning 834.2.1 Federated Learning Training Process 834.2.2 Key Principles of Federated Learning 854.2.3 Comparing Traditional and Federated Learning: Data Management, Privacy, Scalability, and Performance 864.2.4 Applications of Federated Learning 884.3 FL in Healthcare 884.3.1 Need of FL in Healthcare 884.3.2 Types of FL for Healthcare 894.3.3 Role of Federated Learning in Healthcare 904.4 Federated Learning in Healthcare: Case Studies 924.5 Challenges and Ethical Consideration 944.6 Conclusion and Future Scope 96References 975 Integrating Edge Devices and Internet of Medical Things in Modern Healthcare 101Manoj Kumar Patra and Nandita Bhanja Chaudhuri5.1 Introduction 1025.1.1 Importance and Impact on Modern Healthcare 1025.1.2 Historical Context and Evolution of Medical Technology 1035.2 Edge Devices in Healthcare 1035.2.1 Functionalities of Edge Devices in Patient Monitoring 1045.2.2 Edge Device Applications in Healthcare 1045.3 Internet of Medical Things (IoMT) 1055.3.1 IoMT for Enhanced Healthcare Delivery 1065.3.2 Integration of IoMT with Existing Healthcare Systems 1075.4 Benefits of Integrating Edge Devices and IoMT 1085.4.1 Accuracy and Efficiency in Diagnostics and Treatment 1085.4.2 Reduction in Latency and Faster Decision-Making 1095.4.3 Cost-Effectiveness and Resource Optimization in Healthcare 1095.5 Key Technologies Enabling Integration 1105.5.1 Edge Computing 1105.5.2 Data Analytics and Machine Learning for Healthcare Insights 1115.5.3 Communication Protocols and Standards 1115.5.4 Cloud Computing for Data Storage and Processing 1125.6 Applications and Use Cases of Edge Devices and IoMT 1135.6.1 Remote Patient Monitoring and Telemedicine 1135.6.2 Chronic Disease Management 1145.6.3 Emergency Response Systems and Critical Care 1145.6.4 Smart Hospitals and Healthcare Facilities 1155.7 Challenges and Considerations 1165.7.1 Data Privacy and Security Concerns in IoMT and Edge Devices 1165.7.2 Interoperability and Integration with Existing Healthcare Infrastructure 1175.7.3 Scalability and Network Reliability 1175.7.4 Regulatory and Compliance Issues 1185.8 Future Trends and Innovations 1195.8.1 Advances in Edge Computing Technologies and Their Potential Impact 1195.8.2 Emerging Applications of IoMT in Personalized Medicine 1205.8.3 Integration with Artificial Intelligence and Predictive Analytics 1215.8.4 Potential for Blockchain in Securing IoMT Data 1215.9 Conclusion 122References 1236 Cloud Infrastructure and Federated Learning 127Kanishka Gupta, Amit Aylani, Prakash Parmar and Deepak Hajoary6.1 Foundations of the Future: Cloud Infrastructure Meets Federated Learning 1286.1.1 Types of Cloud Deployments 1296.1.2 Services of Cloud Computing 1306.2 What is Federated Learning? 1336.2.1 Mechanics of Federated Learning 1346.3 The Essence of Collaboration: Federated Learning Unveiled 1366.3.1 Concept and Working of Federated Learning 1366.3.2 Types of Federated Learning 1386.4 Harmonizing Cloud and Edge: The Integration Paradigm 1406.4.1 Leveraging Cloud Resources for Federated Learning 1406.4.2 Deployment of Federated Learning Models on the Cloud 1456.4.3 How Federated Learning Models are Deployed on the Cloud 1466.5 Real-World Applications of Federated Learning 1496.6 Conclusion and Future Directions 150References 1517 Machine Learning and Artificial Intelligence Fundamentals for Federated Systems 153N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha and Rajesh Sharma R.7.1 Overview of Machine Learning and Artificial Intelligence 1547.1.1 Definition of Machine Learning 1547.1.2 Definition of Artificial Intelligence 1547.1.3 Importance of ML and AI in Modern Technology 1557.2 Key Concepts in Machine Learning 1557.2.1 Data and Features 1557.2.2 Algorithms and Models 1567.2.3 Training and Testing 1577.3 Fundamentals of Artificial Intelligence 1577.3.1 Neural Networks 1577.3.2 Deep Learning 1577.3.3 Natural Language Processing (NLP) 1587.3.4 Reinforcement Learning 1587.4 Federated Learning 1587.4.1 Definition and Importance 1587.4.2 Architecture of Federated Learning Systems 1597.4.3 Applications of Federated Learning 1597.5 Challenges in Federated Learning 1617.5.1 Data Heterogeneity 1617.5.2 Communication Efficiency 1617.5.3 Privacy and Security 1617.5.4 System and Computational Constraints 1627.6 Key Algorithms for Federated Learning 1627.7 Model Aggregation and Optimization 1647.7.1 Aggregation Techniques 1647.7.2 Optimization Algorithms 1647.8 Conclusion 166References 1668 Reconstructing Healthcare Foundations: Building Blocks of Federated Systems in Medical Technology 171Blessing Takawira and David PooeIntroduction 172Historical Context and Evolution of Healthcare Systems 174Fundamental Concepts of Federated Healthcare Systems 176Technological Foundations 179Building Blocks of Federated Healthcare Systems 180Communication Protocols 181Edge Devices and IoMT Integration 183Privacy and Security Considerations 185Systematic Literature Review Process 187Solutions and Recommendations 188Future Research Directions 191Conclusion 192References 193Key Terms and Definitions 1999 Federated Learning in Brain Tumor Segmentation in Medical Imaging 201Jyoti Kataria and Supriya P. Panda9.1 Introduction to Federated AI in Medical Imaging 2029.1.1 Federated Learning Key Concepts 2039.1.2 Overview of AI Techniques and Key Architectures Used in Segmentation 2059.1.3 Importance of Accurate Segmentation in Diagnosis and Treatment 2069.2 Traditional Segmentation Methods 2069.2.1 Overview of Traditional Techniques 2079.2.2 Advantages of Traditional Methods 2099.2.3 Limitations of Traditional Methods 2099.3 AI-Based Segmentation Methods 2109.3.1 Convolutional Neural Networks (CNNs) 2109.3.1.1 The Benefits of CNN-Based Segmentation 2119.3.2 U-Net and its Variants 2129.3.2.1 U-Net’s Advantages for Brain Tumor Segmentation 2139.3.3 ResNet 50 2149.3.3.1 ResNet’s Advantages for Brain Tumor Segmentation 2159.3.4 Benefits of Federated Learning in Brain Tumor Segmentation 2169.3.5 Comparison of Brain Tumor Segmentation Methods 2189.3.6 Case Studies by Different Institutions 2219.4 Advantages and Challenges of AI-Based Methods 2239.4.1 Advantages 2239.4.2 Challenges 2249.5 Federated Learning Workflow for Brain Tumor Segmentation 2259.6 Notable Projects and Research 2279.6.1 Federated Tumor Segmentation (FeTS) Initiative 2279.6.2 Federated Learning for Healthcare (FL4HC) 2279.6.3 AI for Health by NVIDIA Clara’s 2279.6.4 The Role of FL in BraTS 2289.6.5 Collaborative Research with Hospitals and Universities 2289.6.6 OpenFL by Intel 2289.6.7 Google Health’s Federated Learning Projects 2289.7 Conclusion 2299.8 Future Scope 229References 23010 Disease Prediction and Early Diagnosis Using Federated Models 233Vibha Tiwari, B. K. Mishra, Nitya Hari Das, Balwinder Singh and Harmandeep Kaur10.1 Introduction 23410.1.1 FL in Healthcare 23410.2 Related Works 23610.2.1 Machine Learning (Deep Learning) 23610.2.2 Horizontal FL 23810.2.3 Vertical FL 23810.2.4 Federated Transfer Learning 24010.3 Proposed Method 24110.3.1 Local Machine or Local Hospital Selection for Collecting Dataset 24210.3.2 Upload to the Server 24210.3.3 Client Computation 24210.3.4 Sum-Up All the Devices Dataset 24210.3.5 Update Model 24310.4 Result Discussion 24310.4.1 Dataset Description 24410.5 Conclusion & Future Work 248References 24911 Navigating Bias and Ensuring Fairness in Federated Learning: An In-Depth Exploration of Data Distribution, IID, and Non-IID Challenges 253Vajratiya Vajrobol, Nitisha Aggarwal, Pushkar Baranwal, Geetika Jain Saxena, Amit Pundir and Sanjeev Singh11.1 Introduction to Federated Learning and Data Distribution 25411.2 Understanding Data Bias in Federated Learning 26111.3 Implications of Data Bias in Federated Learning 26311.4 Fairness in Federated Learning 26511.5 Approaches to Address Data Bias and Ensure Fairness 26611.6 Evaluating and Mitigating Bias in Federated Learning 26911.7 Case Studies and Examples 27511.8 Ethical Considerations and Responsible AI 28211.9 Future Directions and Research Challenges 28311.10 Conclusion 284References 285Index 293

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.

• Preface xiii1 Introduction to Federated Intelligent Systems in Healthcare 1Naseem Ahmad1.1 Introduction 21.2 Evolution and Principles of Federated Learning in Healthcare 41.3 Applications of Federated Learning in Healthcare 61.4 Challenges and Limitations of Federated Learning in Healthcare 111.5 Future Directions and Innovations in Federated Healthcare Systems 151.6 Conclusion 20References 212 Federated Autonomous Deep Learning for Distributed Healthcare System 25Rakesh Mohan Pujahari, Rijwan Khan and Satya Prakash Yadav2.1 Introduction 262.2 Background 272.3 Use of Federated Learning 282.4 Smart and Efficient Healthcare Systems: Various Types of Federated Learning 322.5 Healthcare Integrated Learning in IoMT Apps 332.6 Federated Learning Based on Federated Mechanisms and Difficulties in Healthcare Applications 352.6.1 Data Security and Breach 352.6.2 Heterogeneity of Data 382.6.3 Compliance Regulatory Mechanism 392.6.4 Data Governance 392.6.5 Process of Communication Overhead 402.6.6 Model Selection and Aggregation 422.6.7 Annotation and Labeling of Data 432.6.8 Model Drift 452.6.9 Resource Constraints 462.6.10 Bias and Fairness 472.6.11 Interoperability 482.6.12 Engagement and Incentives Related to Patients 492.6.13 Scalability 502.6.14 Considerations Based on Ethics 512.7 Healthcare Issues and Their Solutions Related to Federated Learning 522.8 Directions for Future Use of Federated Learning in the Medical System 542.9 Conclusion 55References 563 Intelligent Fusion: Federated Learning and Blockchain in Sustainable Healthcare 5.0 61Pankaj Kumar Jadwal and Hemant Kumar Saini3.1 Introduction 623.2 Distributed Data in Healthcare 643.2.1 FL Algorithms 643.2.2 Blockchain Approaches 663.2.3 Fusion of BC and FL 663.2.4 Framework/Architecture 673.3 IoHT Applications and Their Wideband Challenges 713.3.1 In Medical Units 723.3.2 Remote Healthcare from Homes 723.3.3 Patient-Generated Data 733.4 Tools 733.5 Case Studies 743.6 Conclusion 76Future Directions 76References 774 Foundations of Federated Intelligent Systems in Healthcare 81Rachna Behl, Indu Kashyap and Neha Garg4.1 Introduction 824.2 Core Concepts of Federated Learning 834.2.1 Federated Learning Training Process 834.2.2 Key Principles of Federated Learning 854.2.3 Comparing Traditional and Federated Learning: Data Management, Privacy, Scalability, and Performance 864.2.4 Applications of Federated Learning 884.3 FL in Healthcare 884.3.1 Need of FL in Healthcare 884.3.2 Types of FL for Healthcare 894.3.3 Role of Federated Learning in Healthcare 904.4 Federated Learning in Healthcare: Case Studies 924.5 Challenges and Ethical Consideration 944.6 Conclusion and Future Scope 96References 975 Integrating Edge Devices and Internet of Medical Things in Modern Healthcare 101Manoj Kumar Patra and Nandita Bhanja Chaudhuri5.1 Introduction 1025.1.1 Importance and Impact on Modern Healthcare 1025.1.2 Historical Context and Evolution of Medical Technology 1035.2 Edge Devices in Healthcare 1035.2.1 Functionalities of Edge Devices in Patient Monitoring 1045.2.2 Edge Device Applications in Healthcare 1045.3 Internet of Medical Things (IoMT) 1055.3.1 IoMT for Enhanced Healthcare Delivery 1065.3.2 Integration of IoMT with Existing Healthcare Systems 1075.4 Benefits of Integrating Edge Devices and IoMT 1085.4.1 Accuracy and Efficiency in Diagnostics and Treatment 1085.4.2 Reduction in Latency and Faster Decision-Making 1095.4.3 Cost-Effectiveness and Resource Optimization in Healthcare 1095.5 Key Technologies Enabling Integration 1105.5.1 Edge Computing 1105.5.2 Data Analytics and Machine Learning for Healthcare Insights 1115.5.3 Communication Protocols and Standards 1115.5.4 Cloud Computing for Data Storage and Processing 1125.6 Applications and Use Cases of Edge Devices and IoMT 1135.6.1 Remote Patient Monitoring and Telemedicine 1135.6.2 Chronic Disease Management 1145.6.3 Emergency Response Systems and Critical Care 1145.6.4 Smart Hospitals and Healthcare Facilities 1155.7 Challenges and Considerations 1165.7.1 Data Privacy and Security Concerns in IoMT and Edge Devices 1165.7.2 Interoperability and Integration with Existing Healthcare Infrastructure 1175.7.3 Scalability and Network Reliability 1175.7.4 Regulatory and Compliance Issues 1185.8 Future Trends and Innovations 1195.8.1 Advances in Edge Computing Technologies and Their Potential Impact 1195.8.2 Emerging Applications of IoMT in Personalized Medicine 1205.8.3 Integration with Artificial Intelligence and Predictive Analytics 1215.8.4 Potential for Blockchain in Securing IoMT Data 1215.9 Conclusion 122References 1236 Cloud Infrastructure and Federated Learning 127Kanishka Gupta, Amit Aylani, Prakash Parmar and Deepak Hajoary6.1 Foundations of the Future: Cloud Infrastructure Meets Federated Learning 1286.1.1 Types of Cloud Deployments 1296.1.2 Services of Cloud Computing 1306.2 What is Federated Learning? 1336.2.1 Mechanics of Federated Learning 1346.3 The Essence of Collaboration: Federated Learning Unveiled 1366.3.1 Concept and Working of Federated Learning 1366.3.2 Types of Federated Learning 1386.4 Harmonizing Cloud and Edge: The Integration Paradigm 1406.4.1 Leveraging Cloud Resources for Federated Learning 1406.4.2 Deployment of Federated Learning Models on the Cloud 1456.4.3 How Federated Learning Models are Deployed on the Cloud 1466.5 Real-World Applications of Federated Learning 1496.6 Conclusion and Future Directions 150References 1517 Machine Learning and Artificial Intelligence Fundamentals for Federated Systems 153N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha and Rajesh Sharma R.7.1 Overview of Machine Learning and Artificial Intelligence 1547.1.1 Definition of Machine Learning 1547.1.2 Definition of Artificial Intelligence 1547.1.3 Importance of ML and AI in Modern Technology 1557.2 Key Concepts in Machine Learning 1557.2.1 Data and Features 1557.2.2 Algorithms and Models 1567.2.3 Training and Testing 1577.3 Fundamentals of Artificial Intelligence 1577.3.1 Neural Networks 1577.3.2 Deep Learning 1577.3.3 Natural Language Processing (NLP) 1587.3.4 Reinforcement Learning 1587.4 Federated Learning 1587.4.1 Definition and Importance 1587.4.2 Architecture of Federated Learning Systems 1597.4.3 Applications of Federated Learning 1597.5 Challenges in Federated Learning 1617.5.1 Data Heterogeneity 1617.5.2 Communication Efficiency 1617.5.3 Privacy and Security 1617.5.4 System and Computational Constraints 1627.6 Key Algorithms for Federated Learning 1627.7 Model Aggregation and Optimization 1647.7.1 Aggregation Techniques 1647.7.2 Optimization Algorithms 1647.8 Conclusion 166References 1668 Reconstructing Healthcare Foundations: Building Blocks of Federated Systems in Medical Technology 171Blessing Takawira and David PooeIntroduction 172Historical Context and Evolution of Healthcare Systems 174Fundamental Concepts of Federated Healthcare Systems 176Technological Foundations 179Building Blocks of Federated Healthcare Systems 180Communication Protocols 181Edge Devices and IoMT Integration 183Privacy and Security Considerations 185Systematic Literature Review Process 187Solutions and Recommendations 188Future Research Directions 191Conclusion 192References 193Key Terms and Definitions 1999 Federated Learning in Brain Tumor Segmentation in Medical Imaging 201Jyoti Kataria and Supriya P. Panda9.1 Introduction to Federated AI in Medical Imaging 2029.1.1 Federated Learning Key Concepts 2039.1.2 Overview of AI Techniques and Key Architectures Used in Segmentation 2059.1.3 Importance of Accurate Segmentation in Diagnosis and Treatment 2069.2 Traditional Segmentation Methods 2069.2.1 Overview of Traditional Techniques 2079.2.2 Advantages of Traditional Methods 2099.2.3 Limitations of Traditional Methods 2099.3 AI-Based Segmentation Methods 2109.3.1 Convolutional Neural Networks (CNNs) 2109.3.1.1 The Benefits of CNN-Based Segmentation 2119.3.2 U-Net and its Variants 2129.3.2.1 U-Net’s Advantages for Brain Tumor Segmentation 2139.3.3 ResNet 50 2149.3.3.1 ResNet’s Advantages for Brain Tumor Segmentation 2159.3.4 Benefits of Federated Learning in Brain Tumor Segmentation 2169.3.5 Comparison of Brain Tumor Segmentation Methods 2189.3.6 Case Studies by Different Institutions 2219.4 Advantages and Challenges of AI-Based Methods 2239.4.1 Advantages 2239.4.2 Challenges 2249.5 Federated Learning Workflow for Brain Tumor Segmentation 2259.6 Notable Projects and Research 2279.6.1 Federated Tumor Segmentation (FeTS) Initiative 2279.6.2 Federated Learning for Healthcare (FL4HC) 2279.6.3 AI for Health by NVIDIA Clara’s 2279.6.4 The Role of FL in BraTS 2289.6.5 Collaborative Research with Hospitals and Universities 2289.6.6 OpenFL by Intel 2289.6.7 Google Health’s Federated Learning Projects 2289.7 Conclusion 2299.8 Future Scope 229References 23010 Disease Prediction and Early Diagnosis Using Federated Models 233Vibha Tiwari, B. K. Mishra, Nitya Hari Das, Balwinder Singh and Harmandeep Kaur10.1 Introduction 23410.1.1 FL in Healthcare 23410.2 Related Works 23610.2.1 Machine Learning (Deep Learning) 23610.2.2 Horizontal FL 23810.2.3 Vertical FL 23810.2.4 Federated Transfer Learning 24010.3 Proposed Method 24110.3.1 Local Machine or Local Hospital Selection for Collecting Dataset 24210.3.2 Upload to the Server 24210.3.3 Client Computation 24210.3.4 Sum-Up All the Devices Dataset 24210.3.5 Update Model 24310.4 Result Discussion 24310.4.1 Dataset Description 24410.5 Conclusion & Future Work 248References 24911 Navigating Bias and Ensuring Fairness in Federated Learning: An In-Depth Exploration of Data Distribution, IID, and Non-IID Challenges 253Vajratiya Vajrobol, Nitisha Aggarwal, Pushkar Baranwal, Geetika Jain Saxena, Amit Pundir and Sanjeev Singh11.1 Introduction to Federated Learning and Data Distribution 25411.2 Understanding Data Bias in Federated Learning 26111.3 Implications of Data Bias in Federated Learning 26311.4 Fairness in Federated Learning 26511.5 Approaches to Address Data Bias and Ensure Fairness 26611.6 Evaluating and Mitigating Bias in Federated Learning 26911.7 Case Studies and Examples 27511.8 Ethical Considerations and Responsible AI 28211.9 Future Directions and Research Challenges 28311.10 Conclusion 284References 285Index 293