Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies
Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies
edited by Gururaj Harinahalli Lokesh, Geetabai S. Hukkeri, N.Z. Jhanjhi and Hong Lin
The Institution of Engineering and Technology, 2024 Cloth: 978-1-83953-945-9 | eISBN: 978-1-83953-946-6
ABOUT THIS BOOK | TOC
ABOUT THIS BOOK
New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.
TABLE OF CONTENTS
Chapter 1: Introduction to federated learning, split learning and splitfed learning
Chapter 2: Splitfed learning processing for IoT and Big Data applications
Chapter 3: Blockchain-driven splitfed learning for data protection in IoT setting
Chapter 4: Splitfed learning methods for natural language processing
Chapter 5: The role of splitfed learning in recommendation systems
Chapter 6: Reconfigurable intelligent surface (RIS)-inspired splitfed learning for over-the-air
Chapter 7: Enhancing computational performance in healthcare through federated learning approach
Chapter 8: Splitfed learning for multimodal emotion detection
Chapter 9: Split federated learning-based educational data analysis
Chapter 10: Splitfed learning for smart transportation
Chapter 11: Splitfed learning for smart grids
Chapter 12: Splitfed learning for smart agriculture
Chapter 13: A case study on splitfed learning implementation