Secure and Efficient Collaborative Machine Learning Frameworks for 6G Intelligent Applications
Wai Yie Leong
Abstract
Collaborative Machine Learning (CML) is an approach that involves multiple entities working together to train or enhance machine learning models. This collaborative effort can be between different devices, organizations, or individuals. The concept is gaining importance in the context of next-generation 6G intelligent applications. The relevant data is distributed across different sources, and it may not be feasible or practical to centralize all the data in one location. CML allows models to be trained on decentralized data sources while ensuring privacy and security. While Collaborative Machine Learning offers numerous advantages, challenges such as communication overhead, security, and trust among participants need to be carefully addressed. Developing robust frameworks and protocols for collaborative learning is an ongoing area of research to fully realize the potential of this approach in next-generation intelligent applications.