Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Gao, Yulan"'
With the burgeon deployment of the fifth-generation new radio (5G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5G NR codebooks incur varying overheads and
Externí odkaz:
http://arxiv.org/abs/2407.05928
The Space-Air-Ground Integrated Network (SAGIN), crucial to the advancement of sixth-generation (6G) technology, plays a key role in ensuring universal connectivity, particularly by addressing the communication needs of remote areas lacking cellular
Externí odkaz:
http://arxiv.org/abs/2407.05571
Autor:
Ren, Chao, Yu, Han, Peng, Hongyi, Tang, Xiaoli, Li, Anran, Gao, Yulan, Tan, Alysa Ziying, Zhao, Bo, Li, Xiaoxiao, Li, Zengxiang, Yang, Qiang
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational
Externí odkaz:
http://arxiv.org/abs/2404.15381
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited. Exi
Externí odkaz:
http://arxiv.org/abs/2403.09153
In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devic
Externí odkaz:
http://arxiv.org/abs/2401.10794
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and the large scale of data owners invo
Externí odkaz:
http://arxiv.org/abs/2308.03945
The paradigm of federated learning (FL) to address data privacy concerns by locally training parameters on resource-constrained clients in a distributed manner has garnered significant attention. Nonetheless, FL is not applicable when not all clients
Externí odkaz:
http://arxiv.org/abs/2307.09977
In this paper, we aim to address the challenge of hybrid mobile edge-quantum computing (MEQC) for sustainable task offloading scheduling in mobile networks. We develop cost-effective designs for both task offloading mode selection and resource alloca
Externí odkaz:
http://arxiv.org/abs/2306.14588
Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research attention. However, the problem of optimizing FL client selection in mo
Externí odkaz:
http://arxiv.org/abs/2305.06865
A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations
Externí odkaz:
http://arxiv.org/abs/2211.02919