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Full Description
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.
The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.
This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.
Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.
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
Chapter 14: Conclusion and future directions
