Hierarchical Adaptive Collaborative Learning: A Distributed Learning Framework for Customized Cloud Services in 6G Mobile Systems

The Fifth Generation (5G) mobile systems support many kinds of intelligent cloud services. The upcoming Sixth Generation (6G) mobile systems aim to provide customized cloud services since the users have different capabilities and needs. However, as an essential scenario in 6G mobile systems, collaborative AI failed to deliver customized cloud services to the users because of system heterogeneity. Due to the lack of efficient resource coordination and service orchestration strategy, collaborative learning suffers from high latency and low resource utilization. This article proposes Hierarchical Adaptive Collaborative Learning, a distributed learning framework for customized cloud services in 6G mobile systems, to improve the training efficiency and resource utilization by dynamically adjusting the collaborative training process according to the service and resources. We introduce the critical scenarios and challenges of distributed learning. Then we present our framework to handle heterogeneous cloud services and resources. Next, we illustrate various model training and collaborating techniques to improve training efficiency. Finally, we provide a case study to show the priority of the modules in our framework and provide future research directions on collaborative learning for customized cloud services in 6G mobile systems.