Elsevier Collaborative Learning for 6G Mobile Wireless Networks

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. Presents state-of-the-art, collaborative learning algorithms, including their principles, advantages, and disadvantages Shows how collaborative learning algorithms can overcome the drawbacks of traditional machine learning algorithms in the context of 6G networks Provides insights into how collaborative learning can enhance the capabilities of 6G networks technical aspects such as resource management, security and privacy, etc. Includes practical use cases where collaborative learning enhances the capabilities of 6G network real-world applications Looks into future trends and potential advances of collaborative learning for 6G