Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Huang, Xumin"'
Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to posses
Externí odkaz:
http://arxiv.org/abs/2407.10980
Autor:
Kang, Jiawen, Zhong, Yue, Xu, Minrui, Nie, Jiangtian, Wen, Jinbo, Du, Hongyang, Ye, Dongdong, Huang, Xumin, Niyato, Dusit, Xie, Shengli
The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual explo
Externí odkaz:
http://arxiv.org/abs/2401.09680
Autor:
Huang, Xumin, Wu, Yuan, Kang, Jiawen, Nie, Jiangtian, Zhong, Weifeng, Kim, Dong In, Xie, Shengli
Metaverse enables users to communicate, collaborate and socialize with each other through their digital avatars. Due to the spatio-temporal characteristics, co-located users are served well by performing their software components in a collaborative m
Externí odkaz:
http://arxiv.org/abs/2308.04914
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. Deploying AIGC service
Externí odkaz:
http://arxiv.org/abs/2307.07146
Autor:
Kang, Jiawen, He, Jiayi, Du, Hongyang, Xiong, Zehui, Yang, Zhaohui, Huang, Xumin, Xie, Shengli
For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significant
Externí odkaz:
http://arxiv.org/abs/2306.03528
Publikováno v:
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, New York City, NY, USA, 2023, pp. 1-10
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices
Externí odkaz:
http://arxiv.org/abs/2301.03062
Autor:
Huang, Xumin, Zhong, Weifeng, Nie, Jiangtian, Hu, Qin, Xiong, Zehui, Kang, Jiawen, Quek, Tony Q. S.
Metaverse as the next-generation Internet provides users with physical-virtual world interactions. To improve the quality of immersive experience, users access to Metaverse service providers (MSPs) and purchase bandwidth resource to reduce the commun
Externí odkaz:
http://arxiv.org/abs/2208.06770
Publikováno v:
2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied to FL to alleviate the communication overheads but current FL with gr
Externí odkaz:
http://arxiv.org/abs/2111.06146
Publikováno v:
In Tunnelling and Underground Space Technology incorporating Trenchless Technology Research December 2024 154
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking
Externí odkaz:
http://arxiv.org/abs/2110.12876