Federated Learning For Digital Healthcare Systems

Download Federated Learning For Digital Healthcare Systems full books in PDF, epub, and Kindle. Read online Federated Learning For Digital Healthcare Systems ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Federated Learning for Digital Healthcare Systems

Author : Agbotiname Lucky Imoize
Publisher : Elsevier
ISBN 13 : 0443138966
Total Pages : 459 pages
Book Rating : 4.4/5 (431 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning for Digital Healthcare Systems by : Agbotiname Lucky Imoize

Download or read book Federated Learning for Digital Healthcare Systems written by Agbotiname Lucky Imoize and published by Elsevier. This book was released on 2024-06-10 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry

Federated Learning

Author : Qiang Yang
Publisher : Springer Nature
ISBN 13 : 3030630765
Total Pages : 291 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning by : Qiang Yang

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.”

Federated Learning and Privacy-Preserving in Healthcare AI

Author : Lilhore, Umesh Kumar
Publisher : IGI Global
ISBN 13 :
Total Pages : 373 pages
Book Rating : 4.3/5 (693 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning and Privacy-Preserving in Healthcare AI by : Lilhore, Umesh Kumar

Download or read book Federated Learning and Privacy-Preserving in Healthcare AI written by Lilhore, Umesh Kumar and published by IGI Global. This book was released on 2024-05-02 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Deep Learning for Healthcare

Author : Amandeep Kaur
Publisher : CRC Press
ISBN 13 : 104012612X
Total Pages : 267 pages
Book Rating : 4.0/5 (41 download)

DOWNLOAD NOW!

Book Synopsis Federated Deep Learning for Healthcare by : Amandeep Kaur

Download or read book Federated Deep Learning for Healthcare written by Amandeep Kaur and published by CRC Press. This book was released on 2024-10-02 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.

Federated Learning Systems

Author : Muhammad Habib ur Rehman
Publisher : Springer Nature
ISBN 13 : 3030706044
Total Pages : 207 pages
Book Rating : 4.0/5 (37 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning Systems by : Muhammad Habib ur Rehman

Download or read book Federated Learning Systems written by Muhammad Habib ur Rehman and published by Springer Nature. This book was released on 2021-06-11 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and AI for Healthcare 5.0

Author : Hassan, Ahdi
Publisher : IGI Global
ISBN 13 :
Total Pages : 413 pages
Book Rating : 4.3/5 (693 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning and AI for Healthcare 5.0 by : Hassan, Ahdi

Download or read book Federated Learning and AI for Healthcare 5.0 written by Hassan, Ahdi and published by IGI Global. This book was released on 2023-12-18 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security

Author : Hassan, Ahdi
Publisher : IGI Global
ISBN 13 :
Total Pages : 372 pages
Book Rating : 4.3/5 (693 download)

DOWNLOAD NOW!

Book Synopsis Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security by : Hassan, Ahdi

Download or read book Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security written by Hassan, Ahdi and published by IGI Global. This book was released on 2024-02-14 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Healthcare sector is experiencing a mindset change 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. Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry. This research book 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 for Internet of Medical Things

Author : Pronaya Bhattacharya
Publisher : CRC Press
ISBN 13 : 1000891313
Total Pages : 308 pages
Book Rating : 4.0/5 (8 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning for Internet of Medical Things by : Pronaya Bhattacharya

Download or read book Federated Learning for Internet of Medical Things written by Pronaya Bhattacharya and published by CRC Press. This book was released on 2023-06-16 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.

Humanity Driven AI

Author : Fang Chen
Publisher : Springer Nature
ISBN 13 : 3030721884
Total Pages : 330 pages
Book Rating : 4.0/5 (37 download)

DOWNLOAD NOW!

Book Synopsis Humanity Driven AI by : Fang Chen

Download or read book Humanity Driven AI written by Fang Chen and published by Springer Nature. This book was released on 2021-12-01 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Digital Infrastructure for the Learning Health System

Author : Institute of Medicine
Publisher : National Academies Press
ISBN 13 : 0309154162
Total Pages : 336 pages
Book Rating : 4.3/5 (91 download)

DOWNLOAD NOW!

Book Synopsis Digital Infrastructure for the Learning Health System by : Institute of Medicine

Download or read book Digital Infrastructure for the Learning Health System written by Institute of Medicine and published by National Academies Press. This book was released on 2011-10-21 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Like many other industries, health care is increasingly turning to digital information and the use of electronic resources. The Institute of Medicine's Roundtable on Value & Science-Driven Health Care hosted three workshops to explore current efforts and opportunities to accelerate progress in improving health and health care with information technology systems.

Artificial Intelligence in Healthcare

Author : Adam Bohr
Publisher : Academic Press
ISBN 13 : 0128184396
Total Pages : 385 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Artificial Intelligence in Healthcare by : Adam Bohr

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Federated Learning Techniques and Its Application in the Healthcare Industry

Author : H L Gururaj
Publisher : World Scientific Publishing Company
ISBN 13 : 9789811287930
Total Pages : 0 pages
Book Rating : 4.2/5 (879 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning Techniques and Its Application in the Healthcare Industry by : H L Gururaj

Download or read book Federated Learning Techniques and Its Application in the Healthcare Industry written by H L Gururaj and published by World Scientific Publishing Company. This book was released on 2024-07-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning is currently an emerging technology in the field of machine learning. Federated Learning is a structure which trains a centralized model for a given assignment, where the data is de-centralized across different edge devices or servers. This enables preservation of the confidentiality of data on various edge devices, as only the updated outcomes of the models are shared with the centralized model. This means the data can remain on each edge device, while we can still train a model using that data.Federated Learning has greatly increased the potential to transmute data in the healthcare industry, enabling healthcare professionals to improve treatment of patients.This book comprises chapters on applying Federated models in the field of healthcare industry.Federated Learning mainly concentrates on securing the privacy of data by training local data in a shared global model without putting the training data in a centralized location. The importance of federated learning lies in its innumerable uses in health care that ranges from maintaining the privacy of raw data of the patients, discover clinically alike patients, forecasting hospitalization due to cardiac events impermanence and probable solutions to the same. The goal of this edited book is to provide a reference guide to the theme.

A Study on Federated Learning Systems in Healthcare

Author : Arthur Smith
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (128 download)

DOWNLOAD NOW!

Book Synopsis A Study on Federated Learning Systems in Healthcare by : Arthur Smith

Download or read book A Study on Federated Learning Systems in Healthcare written by Arthur Smith and published by . This book was released on 2021 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in modern medicine occur because of research, and the most relevant research comes from clinical trials that include a large and diverse patient population. What of the possibility that this could be accomplished by collaborations between different hospital systems across the U.S.? Within each of these systems, there exists a broad and unique range of characteristics (e.g. community or academic, rural or urban, number of beds, specialist availability and services) each with a distinctive patient population. The patient health care record contained within each however, is governed by privacy restrictions as well as a data governance by that particular hospital system. There also exists an abundance of information from the wearable health device sphere: personal fitness trackers such as wrist monitors as well as the physician prescribed cardiac, glucose and oxygen sensors. What if we could collect all this independent and wide-ranging data while maintaining the patient's privacy? The envisioned possibility here in obtaining such a diverse, unbiased collection has the potential to bring enormous advances to modern day medicine, ultimately improving patient care, which as physicians, is our ultimate goal.

Intelligent Healthcare

Author : Chinmay Chakraborty
Publisher : Springer Nature
ISBN 13 : 9811681503
Total Pages : 493 pages
Book Rating : 4.8/5 (116 download)

DOWNLOAD NOW!

Book Synopsis Intelligent Healthcare by : Chinmay Chakraborty

Download or read book Intelligent Healthcare written by Chinmay Chakraborty and published by Springer Nature. This book was released on 2022-06-02 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book Intelligent Healthcare: Infrastructure, Algorithms, and Management® cover a wide range of research topics on innovative intelligent healthcare solutions and advancements with the latest research developments. Data analytics are relevant for healthcare to meet many technical challenges and issues that need to be addressed to realize this potential. The advanced healthcare systems have to be upgraded with new capabilities such as data analytics, machine learning, intelligent decision making, and more professional services. The Internet of Things helps to design and develop intelligent healthcare solutions assisted by security, data analytics, and machine learning. This book will provide federated learning, Data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart healthcare. This book aims to attract works on multidisciplinary research spanning across computer science and engineering, environmental studies, services, urban planning and development, Healthcare, social sciences, and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative learning and computing solutions and big medical data-powered applications to cope with the real-world challenges for building smart healthcare sectors. Main Features: Ø Immersive technologies in healthcare Ø Internet of medical things Ø Federated learning algorithms Ø Explainable AI in Pervasive Healthcare Ø New management principles using biomedical data Ø Secured healthcare management systems This book aims to set up a better understanding of data scientists, researchers, and technologists under innovative digital health. The reader can find out existing research challenges, current market trends, and low-cost technologies to smoothly address the digital health issue.

Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System

Author : Ali Raza
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

DOWNLOAD NOW!

Book Synopsis Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System by : Ali Raza

Download or read book Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System written by Ali Raza and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Digital Data Improvement Priorities for Continuous Learning in Health and Health Care

Author : Institute of Medicine
Publisher : National Academies Press
ISBN 13 : 030925941X
Total Pages : 58 pages
Book Rating : 4.3/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Digital Data Improvement Priorities for Continuous Learning in Health and Health Care by : Institute of Medicine

Download or read book Digital Data Improvement Priorities for Continuous Learning in Health and Health Care written by Institute of Medicine and published by National Academies Press. This book was released on 2013-04-26 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital health data are the lifeblood of a continuous learning health system. A steady flow of reliable data is necessary to coordinate and monitor patient care, analyze and improve systems of care, conduct research to develop new products and approaches, assess the effectiveness of medical interventions, and advance population health. The totality of available health data is a crucial resource that should be considered an invaluable public asset in the pursuit of better care, improved health, and lower health care costs. The ability to collect, share, and use digital health data is rapidly evolving. Increasing adoption of electronic health records (EHRs) is being driven by the implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which pays hospitals and individuals incentives if they can demonstrate that they use basic EHRs in 2011. Only a third had access to the basic features necessary to leverage this information for improvement, such as the ability to view laboratory results, maintain problem lists, or manage prescription ordering. In addition to increased data collection, more organizations are sharing digital health data. Data collected to meet federal reporting requirements or for administrative purposes are becoming more accessible. Efforts such as Health.Data.gov provide access to government datasets for the development of insights and software applications with the goal of improving health. Within the private sector, at least one pharmaceutical company is actively exploring release of some of its clinical trial data for research by others. Digital Data Improvement Priorities for Continuous Learning in Health and Health Care: Workshop Summary summarizes discussions at the March 2012 Institute of Medicine (2012) workshop to identify and characterize the current deficiencies in the reliability, availability, and usability of digital health data and consider strategies, priorities, and responsibilities to address such deficiencies.

Federated Learning

Author : M. Irfan Uddin
Publisher : CRC Press
ISBN 13 : 1040115330
Total Pages : 194 pages
Book Rating : 4.0/5 (41 download)

DOWNLOAD NOW!

Book Synopsis Federated Learning by : M. Irfan Uddin

Download or read book Federated Learning written by M. Irfan Uddin and published by CRC Press. This book was released on 2024-09-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits. The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations. With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios – such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems. Key Features: Provides a comprehensive guide on tools and techniques of federated learning Highlights many practical real-world examples Includes easy-to-understand explanations