no code implementations • 9 Apr 2024 • Pengfei Zhang, Dingzhu Wen, Guangxu Zhu, Qimei Chen, Kaifeng Han, Yuanming Shi
To realize efficient uplink feature aggregation, we allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique.
no code implementations • 2 Apr 2024 • Yuanming Shi, Li Zeng, Jingyang Zhu, Yong Zhou, Chunxiao Jiang, Khaled B. Letaief
Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL.
no code implementations • 25 Mar 2024 • Qi Li, Ye Shi, Yuning Jiang, Yuanming Shi, Haoyu Wang, H. Vincent Poor
The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids.
no code implementations • 18 Oct 2023 • Yuhan Yang, Youlong Wu, Yuning Jiang, Yuanming Shi
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry.
no code implementations • 16 Oct 2023 • Jingyang Zhu, Yuanming Shi, Yong Zhou, Chunxiao Jiang, Wei Chen, Khaled B. Letaief
We first provide a comprehensive study on the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity.
no code implementations • 8 Oct 2023 • Yong Zhou, Yuanming Shi, Haibo Zhou, Jingjing Wang, Liqun Fu, Yang Yang
The explosive growth of smart devices (e. g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data.
no code implementations • 25 Aug 2023 • Jiali Wang, Yuning Jiang, Xin Liu, Ting Wang, Yuanming Shi
In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise.
no code implementations • 17 Aug 2023 • Junkai Qian, Yuning Jiang, Xin Liu, Qing Wang, Ting Wang, Yuanming Shi, Wei Chen
To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed.
no code implementations • 1 Jun 2023 • Dingzhu Wen, Xiaoyang Li, Yong Zhou, Yuanming Shi, Sheng Wu, Chunxiao Jiang
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything.
no code implementations • 4 May 2023 • Yuanming Shi, Shuhao Xia, Yong Zhou, Yijie Mao, Chunxiao Jiang, Meixia Tao
To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
no code implementations • 30 Apr 2023 • Jiali Wang, Yijie Mao, Ting Wang, Yuanming Shi
We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN.
no code implementations • 3 Mar 2023 • Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir
Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency.
no code implementations • 27 Feb 2023 • Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir
Furthermore, based on the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise.
no code implementations • 13 Jan 2023 • Chengzhong Tian, Yijie Mao, Kangchun Zhao, Yuanming Shi, Bruno Clerckx
Numerical results show that by marrying the benefits of RSMA and RIS, the proposed RIS empowered RSMA achieves a better tradeoff between the WSR of IRs and energy harvested at ERs.
no code implementations • 3 Jan 2023 • Yandong Shi, Lixiang Lian, Yuanming Shi, Zixin Wang, Yong Zhou, Liqun Fu, Lin Bai, Jun Zhang, Wei zhang
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
no code implementations • 1 Dec 2022 • Jun Du, Bingqing Jiang, Chunxiao Jiang, Yuanming Shi, Zhu Han
To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels.
no code implementations • 2 Nov 2022 • Dingzhu Wen, Xiang Jiao, Peixi Liu, Guangxu Zhu, Yuanming Shi, Kaibin Huang
To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.
no code implementations • 20 Oct 2022 • Min Fu, Yuanming Shi, Yong Zhou
To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devices for a shared model training.
no code implementations • 19 Oct 2022 • Zhibin Wang, Yapeng Zhao, Yong Zhou, Yuanming Shi, Chunxiao Jiang, Khaled B. Letaief
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation.
no code implementations • 4 Oct 2022 • Zixuan Zhang, Yuning Jiang, Yuanming Shi, Ye Shi, Wei Chen
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits.
1 code implementation • 21 Sep 2022 • Songjie Xie, Shuai Ma, Ming Ding, Yuanming Shi, Mingjian Tang, Youlong Wu
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver.
no code implementations • 13 Aug 2022 • Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, Kai Yang
In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server.
no code implementations • 3 Jul 2022 • Dingzhu Wen, Peixi Liu, Guangxu Zhu, Yuanming Shi, Jie Xu, Yonina C. Eldar, Shuguang Cui
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
no code implementations • 6 Jun 2022 • Zhibin Wang, Yong Zhou, Yuanming Shi, Weihua Zhuang
We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells.
1 code implementation • 31 Mar 2022 • Yuhan Yang, Yong Zhou, Youlong Wu, Yuanming Shi
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them.
1 code implementation • 29 Mar 2022 • Peng Yang, Yuning Jiang, Ting Wang, Yong Zhou, Yuanming Shi, Colin N. Jones
To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
1 code implementation • 24 Jan 2022 • Wenzhi Fang, Ziyi Yu, Yuning Jiang, Yuanming Shi, Colin N. Jones, Yong Zhou
Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence.
no code implementations • 6 Dec 2021 • Yinan Zou, Yong Zhou, Yuanming Shi, Xu Chen
To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas.
no code implementations • 24 Nov 2021 • Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks.
no code implementations • 31 Oct 2021 • Shaoming Huang, Pengfei Zhang, Yijie Mao, Lixiang Lian, Yuanming Shi
Specifically, we theoretically establish the convergence analysis of the FL process, and then apply the proposed device scheduling policy to maximize the number of weighted devices under the FL system latency and sum power constraints.
no code implementations • 20 Aug 2021 • Shuang Liang, Yuanming Shi, Yong Zhou
Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted $l_1$-norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution.
no code implementations • 22 Jun 2021 • Lukuan Xing, Yong Zhou, Yuanming Shi
Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via exploiting the superposition property of multiple-access channels.
no code implementations • 19 Jun 2021 • Yandong Shi, Hayoung Choi, Yuanming Shi, Yong Zhou
Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access.
no code implementations • 15 Jun 2021 • Yandong Shi, Yong Zhou, Yuanming Shi
In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner.
no code implementations • 1 Jun 2021 • Min Fu, Yong Zhou, Yuanming Shi, Wei Chen, Rui Zhang
Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels.
no code implementations • 11 May 2021 • Wenzhi Fang, Yuning Jiang, Yuanming Shi, Yong Zhou, Wei Chen, Khaled B. Letaief
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels.
no code implementations • 11 May 2021 • Wenzhi Fang, Yinan Zou, Hongbin Zhu, Yuanming Shi, Yong Zhou
In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple IoT devices.
no code implementations • 31 Mar 2021 • Lintao Li, Longwei Yang, Xin Guo, Yuanming Shi, Haiming Wang, Wei Chen, Khaled B. Letaief
Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data.
no code implementations • 25 Jan 2021 • Min Fu, Yong Zhou, Yuanming Shi, Ting Wang, Wei Chen
Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data.
Optimize the trajectory of UAV which plays a BS in communication system
no code implementations • 10 Nov 2020 • Zhibin Wang, Jiahang Qiu, Yong Zhou, Yuanming Shi, Liqun Fu, Wei Chen, Khaled B. Lataief
To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements.
no code implementations • 30 Oct 2020 • Shuhao Xia, Jingyang Zhu, Yuhan Yang, Yong Zhou, Yuanming Shi, Wei Chen
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp).
1 code implementation • 15 Jul 2020 • Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis.
no code implementations • 10 Jun 2020 • Yuning Jiang, Junyan Su, Yuanming Shi, Boris Houska
Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges.
no code implementations • 22 May 2020 • Jinglian He, Kaiqiang Yu, Yong Zhou, Yuanming Shi
The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization.
no code implementations • 14 May 2020 • Min Fu, Yong Zhou, Yuanming Shi
In multiple-input multiple-output (MIMO) device-to-device (D2D) networks, interference and rank-deficient channels are the critical bottlenecks for achieving high degrees of freedom (DoFs).
no code implementations • 28 Apr 2020 • Kai Yang, Yong Zhou, Zhanpeng Yang, Yuanming Shi
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e. g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future.
no code implementations • 13 Apr 2020 • Kai Yang, Yuanming Shi, Yong Zhou, Zhanpeng Yang, Liqun Fu, Wei Chen
Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence".
no code implementations • 24 Feb 2020 • Xiangyu Yang, Sheng Hua, Yuanming Shi, Hao Wang, Jun Zhang, Khaled B. Letaief
By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem.
no code implementations • 22 Feb 2020 • Yuanming Shi, Kai Yang, Tao Jiang, Jun Zhang, Khaled B. Letaief
By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
no code implementations • 28 Jan 2020 • Jialin Dong, Jun Zhang, Yuanming Shi, Jessie Hui Wang
In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection.
1 code implementation • 2 Jan 2020 • Xiaojun Yuan, Ying-Jun Angela Zhang, Yuanming Shi, Wenjing Yan, Hang Liu
Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves.
Information Theory Signal Processing Information Theory
no code implementations • 1 Dec 2019 • Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, Qiang Yang
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
no code implementations • 29 Jul 2019 • Kai Yang, Yuanming Shi, Wei Yu, Zhi Ding
Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge.
2 code implementations • 19 Jul 2019 • Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph.
no code implementations • 4 Jul 2019 • Shuhao Xia, Yuanming Shi
Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious.
no code implementations • 26 Apr 2019 • Khaled B. Letaief, Wei Chen, Yuanming Shi, Jun Zhang, Ying-Jun Angela Zhang
The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications.
no code implementations • 31 Dec 2018 • Kai Yang, Tao Jiang, Yuanming Shi, Zhi Ding
Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center.
no code implementations • 18 Dec 2018 • Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples.
no code implementations • 17 Nov 2018 • Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems.
no code implementations • 12 Nov 2018 • Jialin Dong, Yuanming Shi, Zhi Ding
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks.
no code implementations • 24 Oct 2018 • Hao Wang, Fan Zhang, Yuanming Shi, Yaohua Hu
We propose a general formulation of nonconvex and nonsmooth sparse optimization problems with convex set constraint, which can take into account most existing types of nonconvex sparsity-inducing terms, bringing strong applicability to a wide range of applications.
no code implementations • 12 Oct 2018 • Manolis C. Tsakiris, Liangzu Peng, Aldo Conca, Laurent Kneip, Yuanming Shi, Hayoung Choi
This naturally leads to a polynomial system of $n$ equations in $n$ unknowns, which contains $\xi^*$ in its root locus.
no code implementations • 18 Sep 2018 • Jialin Dong, Yuanming Shi
We consider the problem of demixing a sequence of source signals from the sum of noisy bilinear measurements.