no code implementations • 7 May 2024 • Guanqiao Qu, Zheng Lin, Fangming Liu, Xianhao Chen, Kaibin Huang
To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency.
no code implementations • 9 Apr 2024 • Senkang Hu, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
In addition, the single-vehicle autonomous driving systems lack of the ability of collaboration and negotiation with other vehicles, which is crucial for the safety and efficiency of autonomous driving systems.
no code implementations • 9 Apr 2024 • Zihan Fang, Zheng Lin, Zhe Chen, Xianhao Chen, Yue Gao, Yuguang Fang
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs).
1 code implementation • 24 Mar 2024 • Jiarui Hu, Xianhao Chen, Boyin Feng, Guanglin Li, Liangjing Yang, Hujun Bao, Guofeng Zhang, Zhaopeng Cui
Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM).
no code implementations • 19 Mar 2024 • Zheng Lin, Guanqiao Qu, Wei Wei, Xianhao Chen, Kin K. Leung
In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation.
no code implementations • 24 Feb 2024 • Guangyu Zhu, Yiqin Deng, Xianhao Chen, Haixia Zhang, Yuguang Fang, Tan F. Wong
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data.
no code implementations • 1 Feb 2024 • Yuang Zhang, Haonan An, Zhengru Fang, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang
Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.
no code implementations • 3 Jan 2024 • Senkang Hu, Zhengru Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system.
1 code implementation • 28 Nov 2023 • Senkang Hu, Zhengru Fang, Xianhao Chen, Yuguang Fang, Sam Kwong
To address these challenges, we propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception.
no code implementations • 2 Nov 2023 • Zheng Lin, Zhe Chen, Zihan Fang, Xianhao Chen, Xiong Wang, Yue Gao
To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
no code implementations • 28 Sep 2023 • Zheng Lin, Guanqiao Qu, Qiyuan Chen, Xianhao Chen, Zhe Chen, Kaibin Huang
In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs.
no code implementations • 15 Sep 2023 • Senkang Hu, Zhengru Fang, Haonan An, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang
To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.
no code implementations • 17 Aug 2023 • Song Lyu, Zheng Lin, Guanqiao Qu, Xianhao Chen, Xiaoxia Huang, Pan Li
In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks.
no code implementations • 21 Jun 2023 • Zheng Lin, Guanqiao Qu, Xianhao Chen, Kaibin Huang
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence.
no code implementations • 26 Mar 2023 • Zheng Lin, Guangyu Zhu, Yiqin Deng, Xianhao Chen, Yue Gao, Kaibin Huang, Yuguang Fang
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices.
no code implementations • 21 Jul 2022 • Madhureeta Das, Xianhao Chen, Xiaoyong Yuan, Lan Zhang
Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios.
no code implementations • 18 Mar 2021 • Xianhao Chen, Guangyu Zhu, Lan Zhang, Yuguang Fang, Linke Guo, Xinguang Chen
As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19.