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Federated learning for IoT applications

Yadav, Satya Prakash.

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Federated learning for IoT applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated learning for IoT applications/ edited by Satya Prakash Yadav ... [et al.].
其他作者:	Yadav, Satya Prakash.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	viii, 265 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1. Introduction to Federated Learning -- Chapter 2. Federated Learning for IoT Devices -- Chapter 3. Personalized Federated Learning -- Chapter 4. Federated Learning for an IoT Application -- Chapter 5. Some observations on the behaviour of Federated Learning -- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach -- Chapter 7. A prospective study of federated machine learning in medical image fusion -- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture -- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning -- Chapter 10. Federated Learning using Tensor Flow -- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning: Challenges, Opportunities and Open Issues -- Chapter 12. Security Issues & Solutions for Healthcare Informatics -- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions -- Chapter 14. Quantum Federated Learning for Wireless Communications -- Chapter 15. Federated machine learning with data mining in health care -- Chapter 16. Federated Learning for data mining in Healthcare.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-85559-8
ISBN:	9783030855598

Federated learning for IoT applications
Federated learning for IoT applications[electronic resource] /edited by Satya Prakash Yadav ... [et al.]. - Cham :Springer International Publishing :2022. - viii, 265 p. :ill. (some col.), digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8609. - EAI/Springer innovations in communication and computing..

Chapter 1. Introduction to Federated Learning -- Chapter 2. Federated Learning for IoT Devices -- Chapter 3. Personalized Federated Learning -- Chapter 4. Federated Learning for an IoT Application -- Chapter 5. Some observations on the behaviour of Federated Learning -- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach -- Chapter 7. A prospective study of federated machine learning in medical image fusion -- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture -- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning -- Chapter 10. Federated Learning using Tensor Flow -- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning: Challenges, Opportunities and Open Issues -- Chapter 12. Security Issues & Solutions for Healthcare Informatics -- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions -- Chapter 14. Quantum Federated Learning for Wireless Communications -- Chapter 15. Federated machine learning with data mining in health care -- Chapter 16. Federated Learning for data mining in Healthcare.

This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users' privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. Shows how federated learning utilizes data generated by consumer devices without intruding on privacy, allowing machine learning models to deliver personalized services; Analyzes how federated learning provides a privacy-preserving mechanism to effectively leverage decentralized resources inside end-devices to train machine learning models; Presents case studies that provide a tried and tested approaches to resolution of typical problems in federated learning.

ISBN: 9783030855598

Standard No.: 10.1007/978-3-030-85559-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .F43 2022

Dewey Class. No.: 006.31

Federated learning for IoT applications
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520 $a This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users' privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. Shows how federated learning utilizes data generated by consumer devices without intruding on privacy, allowing machine learning models to deliver personalized services; Analyzes how federated learning provides a privacy-preserving mechanism to effectively leverage decentralized resources inside end-devices to train machine learning models; Presents case studies that provide a tried and tested approaches to resolution of typical problems in federated learning.
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