no code implementations • 28 May 2024 • Xiumei Deng, Jun Li, Long Shi, Kang Wei, Ming Ding, Yumeng Shao, Wen Chen, Shi Jin
To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices.
no code implementations • 28 May 2024 • Xiumei Deng, Jun Li, Kang Wei, Long Shi, Zeihui Xiong, Ming Ding, Wen Chen, Shi Jin, H. Vincent Poor
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
no code implementations • 11 May 2024 • Yumeng Shao, Jun Li, Long Shi, Kang Wei, Ming Ding, Qianmu Li, Zengxiang Li, Wen Chen, Shi Jin
To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function.
no code implementations • 9 May 2024 • Jun Li, Weiwei Zhang, Kang Wei, Guangji Chen, Long Shi, Wen Chen
In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs.
no code implementations • 8 Apr 2024 • Jie Zhang, Jun Li, Long Shi, Zhe Wang, Shi Jin, Wen Chen, H. Vincent Poor
By leveraging the power of DT models learned over extensive datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in a new context, e. g., similar tasks with different state and action spaces, compared with DRL.
1 code implementation • 27 Mar 2024 • Long Shi, Lei Cao, Yunshan Ye, Yu Zhao, Badong Chen
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering.
no code implementations • 3 Feb 2024 • Long Shi, Lei Cao, Zhongpu Chen, Badong Chen, Yu Zhao
Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach.
no code implementations • 18 Jan 2024 • Lu Shen, Benjamin Henson, Long Shi, Yuriy Zakharov
In this work we present a full-duplex (FD) underwater acoustic (UWA) communication system simultaneously transmitting and receiving acoustic signals in the same frequency bandwidth.
1 code implementation • 22 Dec 2023 • Long Shi, Lei Cao, Jun Wang, Badong Chen
Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information.
no code implementations • 20 Sep 2023 • Shiying Zhang, Jun Li, Long Shi, Ming Ding, Dinh C. Nguyen, Wuzheng Tan, Jian Weng, Zhu Han
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
no code implementations • 18 Jan 2021 • Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, H. Vincent Poor
Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
no code implementations • 20 Sep 2020 • Chuan Ma, Jun Li, Ming Ding, Long Shi, Taotao Wang, Zhu Han, H. Vincent Poor
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
no code implementations • 11 Apr 2020 • Cheng Wang, Kang Wei, Lingjun Kong, Long Shi, Zhen Mei, Jun Li, Kui Cai
The error correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error correcting codes (ECCs) and log-likelihood-ratios (LLRs) of the read-voltage thresholds.