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.

• 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