no code implementations • 18 Jan 2024 • Jiawen Kang, Yue Zhong, Minrui Xu, Jiangtian Nie, Jinbo Wen, Hongyang Du, Dongdong Ye, Xumin Huang, Dusit Niyato, Shengli Xie
To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
no code implementations • 9 Aug 2023 • Xumin Huang, Yuan Wu, Jiawen Kang, Jiangtian Nie, Weifeng Zhong, Dong In Kim, Shengli Xie
A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost.
no code implementations • 14 Jul 2023 • Xumin Huang, Peichun Li, Hongyang Du, Jiawen Kang, Dusit Niyato, Dong In Kim, Yuan Wu
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models.
no code implementations • 6 Jun 2023 • Jiawen Kang, Jiayi He, Hongyang Du, Zehui Xiong, Zhaohui Yang, Xumin Huang, Shengli Xie
In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool.
no code implementations • 8 Jan 2023 • Peichun Li, Guoliang Cheng, Xumin Huang, Jiawen Kang, Rong Yu, Yuan Wu, Miao Pan
We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints.
no code implementations • 11 Nov 2021 • Peichun Li, Xumin Huang, Miao Pan, Rong Yu
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data.
no code implementations • 19 Oct 2021 • Xumin Huang, Peichun Li, Rong Yu, Yuan Wu, Kan Xie, Shengli Xie
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
no code implementations • IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 • Jiawen Kang, Rong Y u, Xumin Huang, Maoqiang Wu, Sabita Maharjan, Member, Shengli Xie, and Y an Zhang, Senior Member, IEEE
Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.