1 code implementation • 20 Feb 2024 • Yuwen Yang, Yuxiang Lu, Suizhi Huang, Shalayiding Sirejiding, Hongtao Lu, Yue Ding
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets.
no code implementations • 22 Nov 2023 • Yuxiang Lu, Suizhi Huang, Yuwen Yang, Shalayiding Sirejiding, Yue Ding, Hongtao Lu
Moreover, we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates.
no code implementations • 16 Sep 2023 • Yuwen Yang, Chang Liu, Xun Cai, Suizhi Huang, Hongtao Lu, Yue Ding
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy.
no code implementations • 14 Jan 2023 • Yuwen Yang, Feifei Gao, Xiaoming Tao, Guangyi Liu, Chengkang Pan
In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks.
no code implementations • 19 Nov 2022 • Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu
While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level representation learning and permutation-equivariant in node- and edge-level representation learning, their expressive power is commonly limited by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test.
1 code implementation • 1 Nov 2022 • Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu, Yue Ding
2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure.
no code implementations • 28 Oct 2022 • Chang Liu, Yuwen Yang, Xun Cai, Yue Ding, Hongtao Lu
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i. i. d.
no code implementations • 13 Oct 2022 • Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu
The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and low generality.
no code implementations • 24 Nov 2021 • Yuwen Yang, Feifei Gao, Jiang Xue, Ting Zhou, Zongben Xu
In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems.
no code implementations • 6 Jun 2021 • Zhiyan Liu, Yuwen Yang, Feifei Gao, Ting Zhou, Hongbing Ma
In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems.
no code implementations • 6 Sep 2020 • Chenghong Bian, Yuwen Yang, Feifei Gao, Geoffrey Ye Li
In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots.
no code implementations • 18 Jul 2020 • Yuwen Yang, Feifei Gao, Chengwen Xing, Jianping An, Ahmed Alkhateeb
However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML).
no code implementations • 7 Jul 2020 • Yuwen Yang, Jayant Rajgopal
The Traveling Salesman Problem is one of the most intensively studied combinatorial optimization problems due both to its range of real-world applications and its computational complexity.
1 code implementation • 27 Dec 2019 • Yuwen Yang, Feifei Gao, Zhimeng Zhong, Bo Ai, Ahmed Alkhateeb
Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments.
no code implementations • 29 Sep 2019 • Yuwen Yang, Feifei Gao, Cheng Qian, Guisheng Liao
Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively.
no code implementations • 25 Jul 2019 • Yuwen Yang, Hoda Bidkhori, Jayant Rajgopal
Vaccination has been proven to be the most effective method to prevent infectious diseases.