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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. Introduction 2. Fundamentals of 6G Communications and Networking 3. Federated Learning as a Collaborative Learning Algorithm 4. Split Learning: A Cooperative Framework for Resource-Limited 6G Environments 5. Split Federated Learning: An Enhanced Collaborative Learning Algorithm for Resource-Limited 6G Contexts 6. Application of Collaborative Learning in Resource Management for 6G Networks 7. Advanced 6G-enabled Healthcare Solutions with Collaborative Learning 8. Security and Robustness of Collaborative Learning in the Context of 6G 9. Potential of 6G Immersive Technologies through Collaborative Learning 10. Edge Intelligence and Collaborative Learning in 6G Networks 11. Blockchain-powered Collaborative Learning in 6G Wireless Networks 12. Explainable Collaborative Learning in 6G Wireless Networks 13. Open Issues and Concluding Remarks
