Federated Intelligent System for Healthcare
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.
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.
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.
More details
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Persons
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.
Content
Preface xiii
1 Introduction to Federated Intelligent Systems in Healthcare 1
Naseem Ahmad
1.1 Introduction 2
1.2 Evolution and Principles of Federated Learning in Healthcare 4
1.3 Applications of Federated Learning in Healthcare 6
1.4 Challenges and Limitations of Federated Learning in Healthcare 11
1.5 Future Directions and Innovations in Federated Healthcare Systems 15
1.6 Conclusion 20
References 21
2 Federated Autonomous Deep Learning for Distributed Healthcare System 25
Rakesh Mohan Pujahari, Rijwan Khan and Satya Prakash Yadav
2.1 Introduction 26
2.2 Background 27
2.3 Use of Federated Learning 28
2.4 Smart and Efficient Healthcare Systems: Various Types of Federated Learning 32
2.5 Healthcare Integrated Learning in IoMT Apps 33
2.6 Federated Learning Based on Federated Mechanisms and Difficulties in Healthcare Applications 35
2.6.1 Data Security and Breach 35
2.6.2 Heterogeneity of Data 38
2.6.3 Compliance Regulatory Mechanism 39
2.6.4 Data Governance 39
2.6.5 Process of Communication Overhead 40
2.6.6 Model Selection and Aggregation 42
2.6.7 Annotation and Labeling of Data 43
2.6.8 Model Drift 45
2.6.9 Resource Constraints 46
2.6.10 Bias and Fairness 47
2.6.11 Interoperability 48
2.6.12 Engagement and Incentives Related to Patients 49
2.6.13 Scalability 50
2.6.14 Considerations Based on Ethics 51
2.7 Healthcare Issues and Their Solutions Related to Federated Learning 52
2.8 Directions for Future Use of Federated Learning in the Medical System 54
2.9 Conclusion 55
References 56
3 Intelligent Fusion: Federated Learning and Blockchain in Sustainable Healthcare 5.0 61
Pankaj Kumar Jadwal and Hemant Kumar Saini
3.1 Introduction 62
3.2 Distributed Data in Healthcare 64
3.2.1 FL Algorithms 64
3.2.2 Blockchain Approaches 66
3.2.3 Fusion of BC and FL 66
3.2.4 Framework/Architecture 67
3.3 IoHT Applications and Their Wideband Challenges 71
3.3.1 In Medical Units 72
3.3.2 Remote Healthcare from Homes 72
3.3.3 Patient-Generated Data 73
3.4 Tools 73
3.5 Case Studies 74
3.6 Conclusion 76
Future Directions 76
References 77
4 Foundations of Federated Intelligent Systems in Healthcare 81
Rachna Behl, Indu Kashyap and Neha Garg
4.1 Introduction 82
4.2 Core Concepts of Federated Learning 83
4.2.1 Federated Learning Training Process 83
4.2.2 Key Principles of Federated Learning 85
4.2.3 Comparing Traditional and Federated Learning: Data Management, Privacy, Scalability, and Performance 86
4.2.4 Applications of Federated Learning 88
4.3 FL in Healthcare 88
4.3.1 Need of FL in Healthcare 88
4.3.2 Types of FL for Healthcare 89
4.3.3 Role of Federated Learning in Healthcare 90
4.4 Federated Learning in Healthcare: Case Studies 92
4.5 Challenges and Ethical Consideration 94
4.6 Conclusion and Future Scope 96
References 97
5 Integrating Edge Devices and Internet of Medical Things in Modern Healthcare 101
Manoj Kumar Patra and Nandita Bhanja Chaudhuri
5.1 Introduction 102
5.1.1 Importance and Impact on Modern Healthcare 102
5.1.2 Historical Context and Evolution of Medical Technology 103
5.2 Edge Devices in Healthcare 103
5.2.1 Functionalities of Edge Devices in Patient Monitoring 104
5.2.2 Edge Device Applications in Healthcare 104
5.3 Internet of Medical Things (IoMT) 105
5.3.1 IoMT for Enhanced Healthcare Delivery 106
5.3.2 Integration of IoMT with Existing Healthcare Systems 107
5.4 Benefits of Integrating Edge Devices and IoMT 108
5.4.1 Accuracy and Efficiency in Diagnostics and Treatment 108
5.4.2 Reduction in Latency and Faster Decision-Making 109
5.4.3 Cost-Effectiveness and Resource Optimization in Healthcare 109
5.5 Key Technologies Enabling Integration 110
5.5.1 Edge Computing 110
5.5.2 Data Analytics and Machine Learning for Healthcare Insights 111
5.5.3 Communication Protocols and Standards 111
5.5.4 Cloud Computing for Data Storage and Processing 112
5.6 Applications and Use Cases of Edge Devices and IoMT 113
5.6.1 Remote Patient Monitoring and Telemedicine 113
5.6.2 Chronic Disease Management 114
5.6.3 Emergency Response Systems and Critical Care 114
5.6.4 Smart Hospitals and Healthcare Facilities 115
5.7 Challenges and Considerations 116
5.7.1 Data Privacy and Security Concerns in IoMT and Edge Devices 116
5.7.2 Interoperability and Integration with Existing Healthcare Infrastructure 117
5.7.3 Scalability and Network Reliability 117
5.7.4 Regulatory and Compliance Issues 118
5.8 Future Trends and Innovations 119
5.8.1 Advances in Edge Computing Technologies and Their Potential Impact 119
5.8.2 Emerging Applications of IoMT in Personalized Medicine 120
5.8.3 Integration with Artificial Intelligence and Predictive Analytics 121
5.8.4 Potential for Blockchain in Securing IoMT Data 121
5.9 Conclusion 122
References 123
6 Cloud Infrastructure and Federated Learning 127
Kanishka Gupta, Amit Aylani, Prakash Parmar and Deepak Hajoary
6.1 Foundations of the Future: Cloud Infrastructure Meets Federated Learning 128
6.1.1 Types of Cloud Deployments 129
6.1.2 Services of Cloud Computing 130
6.2 What is Federated Learning? 133
6.2.1 Mechanics of Federated Learning 134
6.3 The Essence of Collaboration: Federated Learning Unveiled 136
6.3.1 Concept and Working of Federated Learning 136
6.3.2 Types of Federated Learning 138
6.4 Harmonizing Cloud and Edge: The Integration Paradigm 140
6.4.1 Leveraging Cloud Resources for Federated Learning 140
6.4.2 Deployment of Federated Learning Models on the Cloud 145
6.4.3 How Federated Learning Models are Deployed on the Cloud 146
6.5 Real-World Applications of Federated Learning 149
6.6 Conclusion and Future Directions 150
References 151
7 Machine Learning and Artificial Intelligence Fundamentals for Federated Systems 153
N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha and Rajesh Sharma R.
7.1 Overview of Machine Learning and Artificial Intelligence 154
7.1.1 Definition of Machine Learning 154
7.1.2 Definition of Artificial Intelligence 154
7.1.3 Importance of ML and AI in Modern Technology 155
7.2 Key Concepts in Machine Learning 155
7.2.1 Data and Features 155
7.2.2 Algorithms and Models 156
7.2.3 Training and Testing 157
7.3 Fundamentals of Artificial Intelligence 157
7.3.1 Neural Networks 157
7.3.2 Deep Learning 157
7.3.3 Natural Language Processing (NLP) 158
7.3.4 Reinforcement Learning 158
7.4 Federated Learning 158
7.4.1 Definition and Importance 158
7.4.2 Architecture of Federated Learning Systems 159
7.4.3 Applications of Federated Learning 159
7.5 Challenges in Federated Learning 161
7.5.1 Data Heterogeneity 161
7.5.2 Communication Efficiency 161
7.5.3 Privacy and Security 161
7.5.4 System and Computational Constraints 162
7.6 Key Algorithms for Federated Learning 162
7.7 Model Aggregation and Optimization 164
7.7.1 Aggregation Techniques 164
7.7.2 Optimization Algorithms 164
7.8 Conclusion 166
References 166
8 Reconstructing Healthcare Foundations: Building Blocks of Federated Systems in Medical Technology 171
Blessing Takawira and David Pooe
Introduction 172
Historical Context and Evolution of Healthcare Systems 174
Fundamental Concepts of Federated Healthcare Systems 176
Technological Foundations 179
Building Blocks of Federated Healthcare Systems 180
Communication Protocols 181
Edge Devices and IoMT Integration 183
Privacy and Security Considerations 185
Systematic Literature Review Process 187
Solutions and Recommendations 188
Future Research Directions 191
Conclusion 192
References 193
Key Terms and Definitions 199
9 Federated Learning in Brain Tumor Segmentation in Medical Imaging 201
Jyoti Kataria and Supriya P. Panda
9.1 Introduction to Federated AI in Medical Imaging 202
9.1.1 Federated Learning Key Concepts 203
9.1.2 Overview of AI Techniques and Key Architectures Used in Segmentation 205
9.1.3 Importance of Accurate Segmentation in Diagnosis and Treatment 206
9.2 Traditional Segmentation Methods 206
9.2.1 Overview of Traditional Techniques 207
9.2.2 Advantages of Traditional Methods 209
9.2.3 Limitations of Traditional Methods 209
9.3 AI-Based Segmentation Methods 210
9.3.1 Convolutional Neural Networks (CNNs) 210
9.3.1.1 The Benefits of CNN-Based Segmentation 211
9.3.2 U-Net and its Variants 212
9.3.2.1 U-Net's Advantages for Brain Tumor Segmentation 213
9.3.3 ResNet 50 214
9.3.3.1 ResNet's Advantages for Brain Tumor Segmentation 215
9.3.4 Benefits of Federated Learning in Brain Tumor Segmentation 216
9.3.5 Comparison of Brain Tumor Segmentation Methods 218
9.3.6 Case Studies by Different Institutions 221
9.4 Advantages and Challenges of AI-Based Methods 223
9.4.1 Advantages 223
9.4.2 Challenges 224
9.5 Federated Learning Workflow for Brain Tumor Segmentation 225
9.6 Notable Projects and Research 227
9.6.1 Federated Tumor Segmentation (FeTS) Initiative 227
9.6.2 Federated Learning for Healthcare (FL4HC) 227
9.6.3 AI for Health by NVIDIA Clara's 227
9.6.4 The Role of FL in BraTS 228
9.6.5 Collaborative Research with Hospitals and Universities 228
9.6.6 OpenFL by Intel 228
9.6.7 Google Health's Federated Learning Projects 228
9.7 Conclusion 229
9.8 Future Scope 229
References 230
10 Disease Prediction and Early Diagnosis Using Federated Models 233
Vibha Tiwari, B. K. Mishra, Nitya Hari Das, Balwinder Singh and Harmandeep Kaur
10.1 Introduction 234
10.1.1 FL in Healthcare 234
10.2 Related Works 236
10.2.1 Machine Learning (Deep Learning) 236
10.2.2 Horizontal FL 238
10.2.3 Vertical FL 238
10.2.4 Federated Transfer Learning 240
10.3 Proposed Method 241
10.3.1 Local Machine or Local Hospital Selection for Collecting Dataset 242
10.3.2 Upload to the Server 242
10.3.3 Client Computation 242
10.3.4 Sum-Up All the Devices Dataset 242
10.3.5 Update Model 243
10.4 Result Discussion 243
10.4.1 Dataset Description 244
10.5 Conclusion & Future Work 248
References 249
11 Navigating Bias and Ensuring Fairness in Federated Learning: An In-Depth Exploration of Data Distribution, IID, and Non-IID Challenges 253
Vajratiya Vajrobol, Nitisha Aggarwal, Pushkar Baranwal, Geetika Jain Saxena, Amit Pundir and Sanjeev Singh
11.1 Introduction to Federated Learning and Data Distribution 254
11.2 Understanding Data Bias in Federated Learning 261
11.3 Implications of Data Bias in Federated Learning 263
11.4 Fairness in Federated Learning 265
11.5 Approaches to Address Data Bias and Ensure Fairness 266
11.6 Evaluating and Mitigating Bias in Federated Learning 269
11.7 Case Studies and Examples 275
11.8 Ethical Considerations and Responsible AI 282
11.9 Future Directions and Research Challenges 283
11.10 Conclusion 284
References 285
Index 293
1
Introduction to Federated Intelligent Systems in Healthcare
Naseem Ahmad
Dr. Rehabilitation Psychologist, Amroha, Uttar Pradesh, India
Abstract
Federated learning is an extensive technique that helps organisations protect patients privacy. Training of deep learning model on federated healthcare data has been evaluated through this learning method. Evaluation and transfer of medical data have been justified potentially with the involvement of federated learning techniques. Decentralised training of the deep learning model has been ensured with the involvement of the federated learning technique. The acting of the hospital-to-client model has been ensured with the involvement of the FL technique. While conducting collaboration between different medical institutions secure preservation of patient information has been ensured with the implementation of this method. Enhancement of patient and institutional access to highquality healthcare service has been ensured with better utilisation of federated learning techniques. Great promise for healthcare applications has been ensured due to the presence of the FL method. Improving the quality of data and reducing the risk of incorrect data annotation-related problems are solved successfully with the utilisation of federated learning technique. Data vulnerability and data breach-related problems are also solved successfully with better utilisation of this federated intelligent system. Instrumental and environmental noises are also solved potentially with better implications for federate intelligent systems. This study demonstrates the role of a federated intelligence system in ensuring the standardisation of medical datasets. Data partition-related aspects of the medical industry have been evaluated efficiently with the successful use of federated learning programs in healthcare. Conducting training on the collaborative machine learning model fl helps to create secure pool of information of multiple clients. The intelligence of the healthcare field has been emphasised with the successful implementation of federated learning. Security privacy, stability and reliability of the healthcare industry have been increased potentially with the involvement of federated learning techniques. The development of an entire healthcare management system has been confirmed successfully by evaluating different potential components of federated learning techniques. In addition, comprehensive changes in the operation of the medical field have been ensured with the involvement of federated learning and the internet of medical things that can. The growth and development of healthcare services have been ensured with the involvement of these technologies. Implementation of a federated intelligence system contributes to improving the ability to sense and transmit health updates successfully. Potential biomedical image analysis and security of information have been evaluated authentically due to the presence of IOMT or federated learning method. Distributed learning for the machine learning model of the medical industry has been highlighted due to the presence of federated learning in healthcare.
Keywords: Federated learning, internet of medical things, medical data, machine learning, security privacy, healthcare services, medical dataset and deep learning
1.1 Introduction
Federated learning is used by healthcare federated intelligent systems to train models at the same time across different healthcare data sources, all the while keeping patient information safe. To find out what cooperative learning in medicine can do, where it came from, and what problems it has, we must examine it closely. It is called secondary study when we review and analyze books and research papers that have already been written. The results show that healthcare data can be used to learn useful things for shared smart systems without putting patients' privacy at risk. Still, there are many ways that data and privacy issues are still a problem. In conclusion, for federated learning to fully realize its promise of personalized patient care, it needs unity between different healthcare sectors, new technology, and strong data governance processes. Healthcare professionals can use Federated Intelligent Systems to make the most cutting-edge decisions based on data. These systems can maintain data privacy and accelerate joint model training across multiple separate data sources by using collaborative learning. Using this method, which keeps patient data safe and secure locally, helps to lower the privacy concerns that come with processing and storing data in one place. Wearable tech and digital health records have made the healthcare business generate more data. In response, more complex statistics and personalized care plans have been created. For example, in healthcare, shared intelligence systems can get useful information from patient data without revealing the patients' identities. Together, these technologies allow healthcare institutions to create more accurate diagnosis tools and prediction models. Treatment works better, and is of higher quality. Big data is used in healthcare to create shared intelligence systems that keep patient data safe. This piece examines group learning in healthcare by exploring its current state, problems, and potential future developments. Shared learning is a creative way to keep data private while using multiple data sources to train machine learning models. To get the most out of shared learning, our study aims to guide healthcare managers how to optimize data while ensuring and protecting patient privacy. To make shared learning more useful in healthcare, it's important to know how it can be used in real life. In studies and tests that have led to better patient tracking, sickness prediction, and individual treatment plans, federated learning has been useful. These cases show that strict privacy rules can be followed while shared learning is used to handle complicated and varied healthcare data.
Cancer study centers and hospitals use cooperative learning to make it easier to diagnose cancer. These companies could work together to make models that are more accurate and reliable by sharing changes to models instead of raw patient data. And this allows models to learn from the diverse types of patients and the knowledge of the participants. We can detect and treat cancer faster if we work together, ultimately for the benefit of the people. Scientists are also investigating how joint learning can be utilized to develop programs capable of predicting long-term illnesses [15].
Federated learning can better tell when a patient's state is about to get worse by mixing data from different healthcare sources, like smart tech and electronic health records. This allows doctors to assist the patient sooner, which could improve their health and lower costs. With the help of AI, shared learning, and the Internet of Things (IoT), the future of healthcare looks bright. By letting computers learn from multiple data sources at the same time, this combination could make medical care more accurate and efficient. Federated learning is a huge step forward in the healthcare business because it lets you use huge amounts of data safely without putting patients' privacy at risk. The healthcare industry can't work without strong teamwork, strict obedience to the law, and a steady flow of new ideas. If these problems are fixed, shared learning could make healthcare systems around the world better and personalized treatment even better.
1.2 Evolution and Principles of Federated Learning in Healthcare
Federated learning is a game-changing way to keep private patient data safe when healthcare needs to analyze data together. This new idea strikes a good balance between the strict privacy and data security rules in the healthcare industry and the advantages of big data analytics [16]. The idea behind collaborative learning is decentralized model training. Federated learning saves data close to home, while centralized machine learning uses data from many places to teach a model [1]. Healthcare devices, hospitals, and even clinics use their own data sets to help them learn. These small groups use their own data to train a shared model. They only send the changed model results to a central server, not the original data. There are many good things about this independent model teaching method. First, it makes sure that privacy rules like HIPAA in the US and GDPR in Europe are followed. This makes data breaches much less common. Sensitive patient data probably wouldn't get out while being stored or transported because raw data never leaves the local area. The only thing that is given to someone is the model choices, which are usually less private and nameless.
Central computers can help you keep track of changes to models. To create changes, it combines locally calculated modifications from several devices or institutions into the global model. The local groups receive this combined model back so they may retrain it using their own data. The world model may learn from many different data sources and improve over time if the data is kept hidden. The healthcare sector may gain greatly from federated learning as it allows models to be trained from a variety of data sources [10]. These differences happen because of the different types of patients, healthcare data sources, and patient groups. Federated learning allows teachers from various schools to work together on model lessons. The models are more useful now that they live longer and show a wider range of illnesses and patient results. With federated learning, it's also easier to...
