Rajni Mohana is currently working as Professor, Department of Computer Science, Amity School of Engineering and Technology, AUMP in Amity University Punjab, Mohali, Punjab, India. She has over 17 years of experience in Academia. She received B.Tech degree in Computer Science and Engineering from BPUT, ROURKELA, Orissa, India in 2003 and M.Tech. degree in Computer Science and Engineering from Punjab Technical University, Jalandhar, India in 2009. She has completed Ph.D. degree in Computer Science and Engineering from Jaypee University of Information Technology, Solan, Himachal Pradesh, India in 2013, working on interoperability problems in service-oriented computing. Her research interests are sentiment analysis, cloud computing. She has published her research in various impact factor journals like MDPI Senors, proceedings of national academy of sciences physical sciences. Dr. Rajni has published around 20 papers in various ESCI and Scopus Journals. She has guided two Ph.D students and is currently guiding three more students. She has guided 4 post graduate students. She is also a reviewer for various renowned International journals and International conferences. She was TPC Chair of 2015 International Conference on Image Information Processing at Jaypee University of Information Technology, Waknaghat, Solan, H.P. on 21-24 December 2015 and also served as TPC Co-Chair of 2017 International Conference on Image Information Processing at Jaypee University of Information Technology, Waknaghat, Solan, H.P. on December 21-23, 2017. She has been a guest editor of SCPE and has edited books.
Isabel de la Torre Díez received her M.S. and Ph.D. degrees in telecommunication engineering from the University of Valladolid, Spain, in 2003 and 2010, respectively. Currently, she is a Full Professor in the Department of Signal Theory and Communications, and Telematics Engineering at the University of Valladolid. She is coordinator of the eHealth and Telemedicine Group (GTe) at the University of Valladolid. Her scientific competences are in the area of Telemedicine, Medical Informatics, e-services and m-services. She has published more than 280 articles in SCI journals, peer-reviewed publications, book chapters, books and conferences in the above-mentioned fields. She has participated/coordinated in more than 50 funded European, national and regional research projects. Moreover, she is reviewer in well-known SCI journals like International Journal of Medical Informatics, Journal of Medical Systems, IET Journal, Telemedicine and e-health Journal, Sensors, IEEE Access, etc.
Aman Sharma is currently working as Assistant Professor (SG) in the Department of Computer Science & Engineering at Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh. He has around 8 years of teaching and research experience. He has completed his Ph.D. thesis with title "Machine Learning based Framework for Drug Prediction of Cancerous Genomic Profiles” in Computer Science & Engineering Department from Thapar Institute of Engineering and Technology, Patiala, Punjab. He obtained his M.E. with thesis title "Community detection and analysis of Twitter social data” in Computer Science & Engineering from Thapar Institute of Engineering and Technology, Patiala, Punjab. He has obtained his B.Tech. in Computer Science & Engineering from R.B.I.E.B.T, Mohali, Punjab. He has been a rank holder throughout his studies. He has also obtained A.I.R-1800 in GATE 2013.
"Federated Learning Applications in Healthcare: Empowering Wellness" is an ambitious and timely endeavor aimed at providing a comprehensive exploration of the intersection between federated learning and healthcare.
Federated learning, a decentralized machine learning approach that enables model training across multiple devices or institutions without centralized data sharing, holds immense promise for revolutionizing healthcare by facilitating collaboration, preserving patient privacy, and improving healthcare outcomes. This book aims to unravel the complexities of federated learning and demonstrate its transformative potential in various healthcare applications. Through a combination of theoretical insights, practical guidelines, case studies, and real-world examples, the book aims to empower readers to harness the potential of federated learning to address pressing healthcare challenges effectively.
Whether readers are healthcare professionals seeking to leverage data-driven approaches to improve patient care, researchers exploring innovative solutions at the intersection of machine learning and healthcare, policymakers grappling with regulatory and ethical considerations, or technologists developing cutting-edge healthcare solutions, this book serves as a valuable resource for understanding, implementing, and advancing federated learning in healthcare.
Chapter 1: Introduction to Federated Learning in Healthcare (6,000 words) (Dr. Diksha Hooda, Assistant Professor, JUIT, Solan)
1.1 Overview of Federated Learning
Definition and key concepts
Comparison with traditional machine learning
1.2 The Healthcare Data Landscape
Types of healthcare data
Privacy challenges in healthcare
1.3 Why Federated Learning for Healthcare?
Potential benefits and transformative power
Current limitations and challenges
Chapter 2: Federated Learning Algorithms and Architectures (8,000 words) (Priyanka Mary Mammen, Research Scholart, Laboratory for Advanced Software Systems, UMass Amherst)
2.1 Federated Learning Basics
Federated Averaging (FedAvg)
Model update and aggregation techniques
2.2 Advanced Architectures
Hierarchical federated learning
Cross-silo vs. cross-device federated learning
2.3 Communication Efficiency
Reducing bandwidth consumption
Asynchronous federated learning
Chapter 3: Privacy-Preserving Techniques (8,000 words) (Nicola Rieke, Sr. Solution Architect Manager NVIDIA)
3.1 Differential Privacy in Federated Learning
Mechanisms and trade-offs
3.2 Homomorphic Encryption
Secure computations on encrypted data
3.3 Secure Multi-Party Computation
Enabling computations without data sharing
3.4 Real-World Examples of Privacy Techniques in Healthcare
Case study: Federated learning for diabetic retinopathy diagnosis in hospital networks
Chapter 4: Real-World Applications of Federated Learning in Healthcare (12,000 words) (Peter Kairouz, Google Research)
4.1 Predictive Modeling for Disease Diagnosis
Applications in early detection (e.g., cancer, cardiovascular disease)
4.2 Patient Outcome Prediction
Improving ICU patient outcomes
Example: Federated learning in sepsis prediction across hospitals
4.3 Personalized Treatment Recommendations
Drug dosage recommendations
Adaptive treatment paths based on patient data
4.4 Drug Discovery and Clinical Trials
Collaborative learning for drug discovery
Real-world case study: Federated learning across pharmaceutical companies
Chapter 5: Ethical Considerations in Federated Learning for Healthcare (7,000 words) (Mehryar Mohri, Head, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences)
5.1 Patient Privacy and Data Ownership
Balancing innovation and patient rights
5.2 Algorithmic Bias
Risks of bias in federated learning models
Case study: Disparities in predictive models across demographics
5.3 Fairness in AI Decision-Making
Fair and ethical use of federated models in healthcare
5.4 Mitigating Bias and Ensuring Accountability
Technical approaches to reducing bias in federated models
Chapter 6: Navigating the Regulatory Landscape (7,000 words) (Ananda Theertha Suresh, Google Research, New York)
6.1 Regulatory Requirements for Federated Learning in Healthcare
GDPR, HIPAA, and other relevant regulations
6.2 Compliance Challenges in Multi-Institutional Collaboration
Managing international regulatory differences
6.3 Certification and Validation of Federated Learning Models
Best practices for healthcare AI validation
Chapter 7: Technical Implementation of Federated Learning Systems (10,000 words) (Benjamin S. Glicksber, VP Data Science & Machine Learning, Character Biosciences | Adjunct Professor, Icahn School)
7.1 Infrastructure Requirements
Data storage, cloud, and on-premise systems
7.2 System Design and Deployment
Architectures for scalable federated learning in healthcare
7.3 Practical Tools and Frameworks
Overview of popular frameworks (e.g., TensorFlow Federated, PySyft)
7.4 Case Study: Deploying Federated Learning for a National Healthcare Network
Chapter 8: Case Studies of Federated Learning in Action (10,000 words) (Adrian Nilsson, Fraunhofer-Chalmers Research Centre for Industrial Mathematics )
8.1 Federated Learning for COVID-19 Diagnosis
Collaborative research and model development across hospitals
8.2 Cross-Hospital Collaboration for Oncology Diagnostics
Predictive modeling for cancer detection
8.3 Federated Learning for Drug Discovery
Case study from pharmaceutical industry
8.4 International Case Study: Federated Learning in Low-Resource Settings
Using federated learning to improve diagnostics in rural healthcare centers
Chapter 9: Future Trends and Innovations in Federated Learning for Healthcare (6,000 words) (Bing Luo, Assistant Professor of Data and Computational Science, Duke Kunshan University)
9.1 Emerging Technologies in Federated Learning
Combining federated learning with edge computing, blockchain
9.2 AI and Federated Learning Synergies
How federated learning will integrate with other healthcare technologies
9.3 Predictions for the Future of Healthcare
The future impact of federated learning on personalized medicine, global collaborations, and healthcare delivery
Chapter 10: Conclusion and Call to Action (4,000 words) (Mohammed Aledhari, Assistant Professor, University of North Texas)
10.1 Summary of Key Takeaways
10.2 Driving Innovation in Healthcare with Federated Learning
10.3 Next Steps for Practitioners, Policymakers, and Researchers
Contributors
