This book introduces a novel integration of Federated Learning with the vision of Healthcare 5.0 to enable secure, adaptive, and intelligent health systems. It presents cutting-edge frameworks that support decentralized model training across medical institutions while preserving patient privacy and ensuring compliance with data regulations.
Focusing on real-world use cases, such as predictive diagnostics, edge-based patient monitoring, personalized medicine, and surgical robotics, it bridges theoretical advances with practical implementations. This book provides deep insights into the design of scalable, privacy-preserving artificial intelligence infrastructures suited for cross-institutional collaboration.
It is designed for artificial intelligence researchers, digital health architects, healthcare technologists, and policy advisors. This supports the development of human-centric, resilient, and interoperable smart healthcare ecosystems.
Wasswa Shafik (Member, IEEE) is a Computer Scientist, Information Technologist, and Director of the Dig Connectivity Research Laboratory (DCRLab) in Kampala, Uganda. He holds a Bachelor’s degree in Information Technology from Ndejje University (Uganda), a Master’s in Information Technology Engineering (Computer Networks) from Yazd University (Iran), and a PhD in Computer Science from Universiti Brunei Darussalam (Brunei). His research focuses on developing computationally and statistically efficient models in artificial intelligence and machine learning, with particular interest in Smart Agriculture, Computer Vision, Ecological Informatics, Applied AI, IoT, Cybersecurity, and Smart Computing. Dr. Shafik has authored and edited hundreds of peer-reviewed books, journal articles, and conference papers, including those presented at IEEE conferences and published in prestigious journals. He actively reviews for international journals indexed by Scopus, Compendex, and Web of Science. Academically, he has contributed to courses such as Mathematics for Data Science, Advanced Algorithms, and System Performance and Evaluation. Earlier, he was a Research Associate at Iran’s Intelligent Network Laboratory. His professional roles have also included Community Data Officer at the Programme for Accessible Health, Data Manager at Population Services International, Research Assistant at the Socio-Economic Data Center, Research Lead at TechnoServe, and former CEO of Asmaah Charity Organisation.
Dr. Pushan Kumar Dutta is an Associate Professor Grade at Amity University Kolkata in the Electronics and Communication Engineering department. He holds a Ph.D. from Jadavpur University and completed a post-doctorate as an Erasmus Mundus Scholar under the European Union Leaders Program (2015–2016) at the University of Oradea. His research interests include data mining, AI, edge computing, and predictive analytics, with applications in smart cities, healthcare, and sustainability. Dr. Dutta has published over 114 Scopus-indexed articles and numerous works in IEEE Xplore and Springer Lecture Notes. A recipient of the ‘Mentor of Change’ by NITI Aayog and other awards, he is known for his innovative teaching methods, two Indian patents, and international contributions, including winning an international white paper contest.
Dr. Priya Pattanaik is a lecturer at the Berlin School of Business and Innovation in Germany. Her research skills are primarily in quantitative analysis and developing machine learning algorithms with deep neural networks and graphical models for visual computing, including medical image analysis and disease detection. She worked as a postdoctoral scientist at IMT Atlantique, France, in the Image and Information Processing department of the LaTIM research group, focusing on developing concepts and tools to address one of the great challenges of the Musculoskeletal (MSK) field: understanding and exploiting the link between the shape and function of a joint (2020-2022). She also worked as a postdoctoral fellow in collaboration with a range of academic institutions and industrial partners, such as Télécom SudParis, the University of Saclay, a team from the Centre for Mathematical Morphology of Mines ParisTech, and the company TRIBVN (2019). In March 2019, she successfully defended her doctoral thesis, which focuses on the use of machine learning for classifying microscopic blood smear images to detect malaria early. She has numerous publications in high-impact SCI and Scopus-indexed research journals and conferences.
