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 due to the substantial volume of data transfer and privacy concerns. 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. Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to collaborative learning and its potential role in the development of 6G, by explaining the principles and presenting methods, algorithms and uses cases.

• Presents state-of-the-art collaborative learning algorithms - 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 insight into how collaborative learning can enhance the capabilities of 6G networks technical aspects such as resource management, security and privacy, etc.
• Practical use cases where collaborative learning enhances the capabilities of 6G network real-world applications
• Insight into future trends and potential advances of collaborative learning for 6G