Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G’s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced algorithms and frameworks to enable network elements to perceive, reason, and act autonomously in dynamic and unpredictable environments. However, traditional machine learning methods rely on centralized data collection and processing, making it a limitation for large-scale applications.
Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Contributors - p. xv 8. Biography - p. xix 9. Preface - p. xxi 10. Chapter 1: Fundamentals of 6G communications and networking - p. 1 11. Chapter 2: Federated learning as a collaborative learning algorithm in 6G - p. 29 12. Chapter 3: Split learning: a cooperative framework for resource-limited 6G environments - p. 41 13. Chapter 4: Split federated learning: an enhanced collaborative learning algorithm for ResourceLimited 6G contexts - p. 51 14. Chapter 5: Application of collaborative learning in resource management for 6G networks - p. 85 15. Chapter 6: Advanced 6G-enabled healthcare solutions with collaborative learning - p. 109 16. Chapter 7: Security and robustness of collaborative learning in the context of 6G - p. 135 17. Chapter 8: Potential of 6G immersive technologies through collaborative learning - p. 165 18. Chapter 9: Edge intelligence and collaborative learning in 6G networks - p. 179 19. Chapter 10: Blockchain-powered collaborative learning in 6G wireless network - p. 201 20. Conclusion - p. 245 21. Index - p. 247
Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G’s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced algorithms and frameworks to enable network elements to perceive, reason, and act autonomously in dynamic and unpredictable environments. However, traditional machine learning methods rely on centralized data collection and processing, making it a limitation for large-scale applications.
Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Contributors - p. xv 8. Biography - p. xix 9. Preface - p. xxi 10. Chapter 1: Fundamentals of 6G communications and networking - p. 1 11. Chapter 2: Federated learning as a collaborative learning algorithm in 6G - p. 29 12. Chapter 3: Split learning: a cooperative framework for resource-limited 6G environments - p. 41 13. Chapter 4: Split federated learning: an enhanced collaborative learning algorithm for ResourceLimited 6G contexts - p. 51 14. Chapter 5: Application of collaborative learning in resource management for 6G networks - p. 85 15. Chapter 6: Advanced 6G-enabled healthcare solutions with collaborative learning - p. 109 16. Chapter 7: Security and robustness of collaborative learning in the context of 6G - p. 135 17. Chapter 8: Potential of 6G immersive technologies through collaborative learning - p. 165 18. Chapter 9: Edge intelligence and collaborative learning in 6G networks - p. 179 19. Chapter 10: Blockchain-powered collaborative learning in 6G wireless network - p. 201 20. Conclusion - p. 245 21. Index - p. 247
