Federated Learning for IoT Applications


Federated Learning for IoT Applications
Authors: Satya Prakash Yadav
Year: 2022
Publisher: Springer
Language: English
ISBN 13: 9783030855581
ISBN 10: 3030855589
Categories: Computers
Pages: 320 / 319

Availability: 5000 in stock

SKU: 9783030855581 Category:

Federated Learning for IoT Applications Satya Prakash Yadav, Bhoopesh Singh Bhati, Dharmendra Prasad Mahato, Sachin Kumar
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.  Categories:
273 / 269
ISBN 10:
ISBN 13:
EAI/Springer Innovations in Communication and Computing
58 MB


There are no reviews yet.

Only logged in customers who have purchased this product may leave a review.

Shopping Cart
Federated Learning for IoT Applications

Availability: 5000 in stock