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Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach
In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing, and the accumulated user experience is essential to be considered.
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence.
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
FedPEAT uses adapters, emulators, and PEFT for federated model tuning, enhancing model privacy and memory efficiency.
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture.
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning
Nevertheless, as real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Stations (MMBS) for computation.
Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization.
User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks
In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy.
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications
In the DL stage, the larger-size 3D virtual objects need to be transmitted back to the XUs.
Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing
The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences.
Time Minimization in Hierarchical Federated Learning
Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts.
Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Being able to access scenes and information associated with the physical world, in the Metaverse in real-time and under mobility, is essential in developing a highly accessible, interactive and interconnective experience for all users.
