Federated learning for IoT applications / Satya Prakash Yadav, Bhoopesh Singh Bhati, Dharmendra Prasad Mahato, Sachin Kumar, editors.
Yadav, Satya Prakash, editor.; Bhati, Bhoopesh Singh, editor.; Mahato, Dharmendra Prasad, editor.; Kumar, Sachin, editor.
2022
Q325.5 .F43 2022
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Title
Federated learning for IoT applications / Satya Prakash Yadav, Bhoopesh Singh Bhati, Dharmendra Prasad Mahato, Sachin Kumar, editors.
ISBN
9783030855598 (electronic bk.)
3030855597 (electronic bk.)
9783030855581
3030855589
DOI
https://doi.org/10.1007/978-3-030-85559-8
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (some color).
Item Number
10.1007/978-3-030-85559-8 doi
Call Number
Q325.5 .F43 2022
Dewey Decimal Classification
006.3/1
Summary
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 9, 2022).
Added Author
Yadav, Satya Prakash, editor.
Bhati, Bhoopesh Singh, editor.
Mahato, Dharmendra Prasad, editor.
Kumar, Sachin, editor.
Series
EAI/Springer innovations in communication and computing.
Available in Other Form
Federated learning for IoT applications.
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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.

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