Federated Learning And Privacy Preserving In Healthcare Ai
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Federated Learning And Privacy Preserving In Healthcare Ai
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Federated Learning And Privacy Preserving In Healthcare Ai written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Medical categories.
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2021-06-11
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Technology & Engineering categories.
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Multiple Perspectives On Artificial Intelligence In Healthcare
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Author : Mowafa Househ
language : en
Publisher: Springer Nature
Release Date : 2021-08-05
Multiple Perspectives On Artificial Intelligence In Healthcare written by Mowafa Househ and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-05 with Technology & Engineering categories.
This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare. It does so by approaching the topic from multiple perspectives, e.g. the nursing, consumer, medical practitioner, healthcare manager, and data analyst perspective. It covers human factors research, discusses patient safety issues, and addresses ethical challenges, as well as important policy issues. By reporting on cutting-edge research and hands-on experience, the book offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes. It will also benefit students and researchers whose work involves artificial intelligence-related research issues in healthcare.
Federated Learning And Ai For Healthcare 5 0
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Author : Hassan, Ahdi
language : en
Publisher: IGI Global
Release Date : 2023-12-18
Federated Learning And Ai For Healthcare 5 0 written by Hassan, Ahdi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-18 with Medical categories.
The Healthcare sector is evolving with Healthcare 5.0, promising better patient care and efficiency. However, challenges like data security and analysis arise due to increased digitization. Federated Learning and AI for Healthcare 5.0 offers solutions, explaining cloud computing's role in managing data and advocating for security measures. It explores federated learning's use in maintaining data privacy during analysis, presenting practical cases for implementation. The book also addresses emerging tech like quantum computing and blockchain-based services, envisioning an innovative Healthcare 5.0. It empowers healthcare professionals, IT experts, and data scientists to leverage these technologies for improved patient care and system efficiency, making Healthcare 5.0 secure and patient centric.
The Algorithmic Foundations Of Differential Privacy
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Author : Cynthia Dwork
language : en
Publisher:
Release Date : 2014
The Algorithmic Foundations Of Differential Privacy written by Cynthia Dwork and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Humanity Driven Ai
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Author : Fang Chen
language : en
Publisher: Springer Nature
Release Date : 2021-12-01
Humanity Driven Ai written by Fang Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-01 with Computers categories.
Artificial Intelligence (AI) is changing the world around us, and it is changing the way people are living, working, and entertaining. As a result, demands for understanding how AI functions to achieve and enhance human goals from basic needs to high level well-being (whilst maintaining human health) are increasing. This edited book systematically investigates how AI facilitates enhancing human needs in the digital age, and reports on the state-of-the-art advances in theories, techniques, and applications of humanity driven AI. Consisting of five parts, it covers the fundamentals of AI and humanity, AI for productivity, AI for well-being, AI for sustainability, and human-AI partnership. Humanity Driven AI creates an important opportunity to not only promote AI techniques from a humanity perspective, but also to invent novel AI applications to benefit humanity. It aims to serve as the dedicated source for the theories, methodologies, and applications on humanity driven AI, establishing state-of-the-art research, and providing a ground-breaking book for graduate students, research professionals, and AI practitioners.
Convergence Of Internet Of Medical Things Iomt And Generative Ai
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Author : Kumar, V. Vinoth
language : en
Publisher: IGI Global
Release Date : 2025-02-05
Convergence Of Internet Of Medical Things Iomt And Generative Ai written by Kumar, V. Vinoth and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-05 with Computers categories.
In recent years, the convergence of the Internet of Medical Things (IoMT) and Generative Artificial Intelligence (AI) has revolutionized healthcare delivery, offering unprecedented opportunities to enhance patient care, improve clinical outcomes, and optimize healthcare systems globally. IoMT based smart healthcare system is a collection of several smart medical equipment including wearable devices and apps connected within the network to provide health information. Generative AI revolutionizes global health in areas like medical data synthesis, image enhancement, disease prediction and diagnosis, drug discovery, medical documentation, and personalized healthcare. It offers opportunities to overcome data scarcity and privacy concerns through synthetic data generation and supports accurate disease interpretation and diagnosis through image quality enhancement. However, as IoMT and Generative AI continue to be used across healthcare systems, it is critical to examine their impact on global health, considering diverse socio-economic contexts, cultural sensitivities, and ethical implications. Convergence of Internet of Medical Things (IoMT) and Generative AI explores recent advancements in IoMT and generative AI, with a focus on state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those technologies. It provides insights on how to develop IoMT and generative AI technologies to meet smart business and society development demands, especially in the healthcare field. This book covers topics such as medical technology, wearable technology, and data science, and is a useful resource for medical and healthcare professionals, scientists, engineers, academicians, and researchers.
Proceedings Of The International Conference On Sustainability Innovation In Computing And Engineering Icsice 24
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Author : S. Kannadhasan
language : en
Publisher: Springer Nature
Release Date : 2025-06-24
Proceedings Of The International Conference On Sustainability Innovation In Computing And Engineering Icsice 24 written by S. Kannadhasan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
This is an open access book. The International Conference on Sustainability Innovation in Computing and Engineering is a distinguished event that brings together leading experts, researchers, practitioners, and innovators to explore the transformative role of computing and engineering in advancing sustainable solutions. In today’s world, where environmental challenges are intensifying, the need for technological innovation in addressing sustainability issues has never been more urgent. This conference serves as a dynamic platform for sharing groundbreaking research, showcasing innovative technologies, and fostering cross-disciplinary collaborations to accelerate sustainable development. With a focus on integrating sustainability into the core of computing and engineering practices, this conference will delve into a wide array of topics such as sustainable computing technologies, energy-efficient systems, green engineering practices, and the role of data science in promoting sustainability. It will also highlight the latest advancements in areas like artificial intelligence, smart systems, and digital solutions that contribute to environmental stewardship and social equity. The conference aims to bridge the gap between theoretical research and practical application, empowering participants to develop actionable strategies and innovative solutions that can be deployed in real-world scenarios. By facilitating robust discussions and knowledge exchange, the conference seeks to inspire new ideas, foster collaboration, and catalyze the development of technologies that not only enhance efficiency and performance but also contribute to a more sustainable future. It is an honor to host a gathering of visionary leaders in computing and engineering, whose expertise and insights will guide the global movement toward a greener, more sustainable world.
Future Of Ai In Medical Imaging
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-11
Future Of Ai In Medical Imaging written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-11 with Computers categories.
Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.