no code implementations • 16 May 2024 • Haonan An, Guang Hua, Zhiping Lin, Yuguang Fang
1) We develop an extractor-gradient-guided (EGG) remover and show its effectiveness when the extractor uses ReLU activation only.
no code implementations • 25 Apr 2024 • Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Yuguang Fang, Dong In Kim, Xuemin, Shen
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions.
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).
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 • 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 • 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 • 15 Apr 2023 • Zhenxiao Zhang, Yuanxiong Guo, Yuguang Fang, Yanmin Gong
In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy.
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 • 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.
no code implementations • 13 Jan 2021 • Dian Shi, Liang Li, Rui Chen, Pavana Prakash, Miao Pan, Yuguang Fang
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications.
no code implementations • 21 Dec 2020 • Rui Chen, Liang Li, Kaiping Xue, Chi Zhang, Miao Pan, Yuguang Fang
To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices.
no code implementations • 9 Aug 2020 • Jian Li, Lan Zhang, Kaiping Xue, Yuguang Fang
Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternative optimization method to decouple it into multiple sub-problems.