6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,anywhere.More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life.As the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge.However,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and interoperability.To this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and Metaverse.Specifically,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse.Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions.Furthermore,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data.Finally,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

Large-Scale Model Meets Federated Learning:A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model

The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and updated.The traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data leakage.The combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing mode.Therefore,we propose a hierarchical hybrid distributed training mechanism for I2LSM.Firstly,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training architecture.Then,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated learning.Further,we propose an adaptive compressed sensing algorithm to reduce the communication overhead.Finally,we analyze the convergence of the H2Fed algorithm.Experimental results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.