000			03297nam a2200385 a 4500
001			00013722
003			WSP
005			20260416153413.0
007			cr |nu|||unuuu
008			240412s2024 si o 001 0 eng d
020			_a9789811287947 _q(ebook)
020			_a9811287945 _q(ebook)
020			_z9789811287930 _q(hbk.)
040			_aWSPC _beng _cWSPC
050		4	_aR858
072		7	_aCOM _x094000 _2bisacsh
072		7	_aMED _x058000 _2bisacsh
072		7	_aMED _x035000 _2bisacsh
082	0	4	_a610.285 _223
049			_aMAIN
245	0	0	_aFederated learning techniques and its application in the healthcare industry / _ceditors, H.L. Gururaj ... [et al.].
260			_aSingapore : _bWorld Scientific, _cc2024.
300			_a1 online resource (viii, 226 p.)
500			_aIncludes index.
505	0		_aFederated learning techniques and its application in the healthcare industry -- Federated learning and its classifications -- Federated learning : revolutionizing financial insights and security in the digital age -- Review : recent applications on federated learning -- A review on various protocols in federated learning -- Fundamental theory of federated learning, protocols and enabling technologies for healthcare -- Federated learning for securing data access and its applications in healthcare -- A comprehensive study on time series analysis in healthcare -- Federated learning using TensorFlow -- Opportunities and challenges in federated learning -- Future directions and advances in federated learning.
520			_a"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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aMedical informatics _xTechnological innovations.
650		0	_aMachine learning _xMedical applications.
655		0	_aElectronic books.
700	1		_aGururaj, H. L., _d1988-
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/13722#t=toc
942			_cE-BOOK
999			_c49767 _d49767