Split Federated Learning for Secure IoT Applications
Split Federated Learning for Secure IoT Applications
Concepts, frameworks, applications and case studies
Lin, Hong; Jhanjhi, NZ; Hukkeri, Geetabai S.; Lokesh, Gururaj Harinahalli
Institution of Engineering and Technology
11/2024
274
Dura
9781839539459
Pré-lançamento - envio 15 a 20 dias após a sua edição
Chapter 2: SplitFed Learning Processing for IoT and Bigdata 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 Cities
Chapter 12: SplitFed Learning for Smart Agriculture
Chapter 13: A Case Study on SplitFed Learning Implementation
Chapter 14: Conclusion and Future Directions
Chapter 2: SplitFed Learning Processing for IoT and Bigdata 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 Cities
Chapter 12: SplitFed Learning for Smart Agriculture
Chapter 13: A Case Study on SplitFed Learning Implementation
Chapter 14: Conclusion and Future Directions