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Federated learning techniques and its application in the healthcare industry / editors, H.L. Gururaj ... [et al.].

Contributor(s): Material type: TextPublication details: Singapore : World Scientific, c2024.Description: 1 online resource (viii, 226 p.)ISBN:
  • 9789811287947
  • 9811287945
Subject(s): Genre/Form: DDC classification:
  • 610.285 23
LOC classification:
  • R858
Online resources:
Contents:
Federated 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.
Summary: "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"-- Publisher's website.
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Electronic Books CUTN Central Library 610.285 (Browse shelf(Opens below)) Link to resource Available EB04958

Includes index.

Federated 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.

"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"-- Publisher's website.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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