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 ...
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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.
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Add this copy of Federated Learning for Internet of Medical Things: to cart. $55.78, like new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2024 by CRC Press.
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Fine. Trade paperback (US). Glued binding. 290 p. Contains: Unspecified, Line drawings, black & white, Line drawings, color. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Federated Learning for Internet of Medical Things: to cart. $56.34, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2024 by CRC Press.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. Trade paperback (US). Glued binding. 290 p. Contains: Unspecified, Line drawings, black & white, Line drawings, color. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Federated Learning for Internet of Medical Things: to cart. $56.35, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2024 by CRC Press.
Add this copy of Federated Learning for Internet of Medical Things: to cart. $74.06, new condition, Sold by Booksplease rated 4.0 out of 5 stars, ships from Southport, MERSEYSIDE, UNITED KINGDOM, published 2024 by CRC Press.
