Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Tang, Shunpu"'
Semantic communication (SemCom) enhances transmission efficiency by sending only task-relevant information compared to traditional methods. However, transmitting semantic-rich data over insecure or public channels poses security and privacy risks. Th
Externí odkaz:
http://arxiv.org/abs/2405.09234
Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data in centralized training methods. We consider federated learning (FL) to
Externí odkaz:
http://arxiv.org/abs/2404.18848
Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels
Externí odkaz:
http://arxiv.org/abs/2404.12170
Recently, learning-based semantic communication (SemCom) has emerged as a promising approach in the upcoming 6G network and researchers have made remarkable efforts in this field. However, existing works have yet to fully explore the advantages of th
Externí odkaz:
http://arxiv.org/abs/2403.20237
Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communica
Externí odkaz:
http://arxiv.org/abs/2304.09438
In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can collaboratively train a shared model without compromising data privacy. However
Externí odkaz:
http://arxiv.org/abs/2110.14937
Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS),
Externí odkaz:
http://arxiv.org/abs/2106.04043
In this paper, we study how to optimize the federated edge learning (FEEL) in UAV-enabled Internet of things (IoT) for B5G/6G networks, from a deep reinforcement learning (DRL) approach. The federated learning is an effective framework to train a sha
Externí odkaz:
http://arxiv.org/abs/2101.12472
Autor:
Chen, Lunyuan, Tang, Shunpu, Balasubramanian, Venki, Xia, Junjuan, Zhou, Fasheng, Fan, Lisheng
Publikováno v:
In Computer Communications 1 October 2022 194:180-188
Publikováno v:
In Physical Communication April 2021 45