Federated Learning and AI for Healthcare 5.0

Addresses the security and privacy concerns of cloud-based healthcare systems. The book offers invaluable strategies, including security primitives, trust-based architectures, privacy models, and compliance standards, ensuring the protection of sensitive patient data while enabling secure data sharing and collaboration.

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The Healthcare sector is experiencing a change in thinking with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Federated Learning and AI for Healthcare 5.0 presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry; it dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes. Federated Learning and AI for Healthcare 5.0 encourages readers to take initiative and address the security and privacy concerns of cloud-based healthcare systems. It offers invaluable strategies, including security primitives, trust-based architectures, privacy models, and compliance standards, ensuring the protection of sensitive patient data while enabling secure data sharing and collaboration within the healthcare ecosystem. In-depth exploration of federated learning in healthcare empowers professionals with a comprehensive understanding of this distributed machine learning approach, preserving data privacy during analysis. Through practical case studies and simulations, readers gain actionable insights to implement federated learning models and frameworks, bringing tangible improvements to real-world healthcare 5.0 scenarios. The book explores emerging technologies like quantum computing, blockchain-based FL cloud services, and intelligent SaaS APIs, envisioning a future where these innovations redefine healthcare 5.0 and lead to groundbreaking advancements. Federated Learning and AI for Healthcare 5.0 serves as an indispensable resource, empowering healthcare professionals, IT experts, data scientists, and academicians to navigate the complexities of modern healthcare, leveraging innovative technologies to revolutionize patient care and system efficiency. With its comprehensive approach and practical insights, this book stands at the forefront of advancing Healthcare 5.0 into a more secure, efficient, and patient-centric era.