Название: Federated Intelligent System for Healthcare: A Practical GuideАвтор: S. Rakesh Kumar, N. Gayathri, Seifedine Kadry
Издательство: Wiley
Год: 2025
Страниц: 313
Язык: английский
Формат: pdf (true), epub
Размер: 32.1 MB
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 (FL) 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 (AI) and Machine Learning (ML) 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.
Federated Learning (FL) is a notable tool for a reading paradigm designed to train models collaboratively through decentralized devices or servers holding a network of information samples without changing them. This approach addresses key challenges in data privacy, security, and utilization. Unlike traditional centralized tool learning, where data is aggregated into a central server, FL allows model training on network data, thereby mitigating privacy risks and reducing latency and bandwidth consumption. This explores critical concepts of tool learning and artificial intelligence as applied to federated systems. It delves into the architectural framework of Federated Learning, highlighting its core components, including the network training process, aggregation algorithms, and communication protocols. It also covers several types of Federated Learning, which encompass horizontal FL, vertical FL, and federated transfer learning, emphasizing their applicability based on the nature of data distribution across clients. Furthermore, the discussion extends to the key challenges faced by federated systems, such as dealing with non-independent and identically distributed (IID) statistics, ensuring model robustness against adverse attacks, and maintaining efficient data communication. Solutions to these challenges, including federated averaging, differential privacy, and secure multi-party computation, are also reviewed. This offers a comprehensive assessment of how tool learning and AI principles underpin the Federated Learning framework, fostering advancements in efficient, privacy-preserving, and collaborative learning systems. Through this examination, the potential of Federated Learning to revolutionize industries reliant on extensive, distributed datasets is underscored, paving the way for innovative applications in healthcare, finance, and beyond. Machine Learning (ML) is a specialized area within Artificial Intelligence focused on creating algorithms that allow computer systems to learn from data and make predictions or decisions without being explicitly programmed. Unlike conventional programming, where explicit rules are provided, ML algorithms detect patterns in data and use those patterns to make decisions.
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.
Contents:
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