no code implementations • 1 Dec 2023 • Yuyi Mao, Xianghao Yu, Kaibin Huang, Ying-Jun Angela Zhang, Jun Zhang
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
1 code implementation • 6 Sep 2023 • Chang Cai, Xiaojun Yuan, Ying-Jun Angela Zhang
In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i. e., feature encoding and classification) and communication (i. e., precoding) modules are designed with the same goal of inference accuracy maximization.
no code implementations • 19 Jun 2023 • Hang Liu, Jia Yan, Ying-Jun Angela Zhang
Consequently, relying solely on communication noise, as done in the multiple-input single-output system, cannot meet high privacy requirements, and a device-side privacy-preserving mechanism is necessary for optimal DP design.
no code implementations • 12 Sep 2022 • Zheyuan Yang, Suzhi Bi, Ying-Jun Angela Zhang
In emergency scenarios, unmanned aerial vehicles (UAVs) can be deployed to assist localization and communication services for ground terminals.
no code implementations • 26 Jul 2022 • Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.
no code implementations • 6 Sep 2021 • Hang Liu, Zehong Lin, Xiaojun Yuan, Ying-Jun Angela Zhang
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.
1 code implementation • 20 Jul 2021 • Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large.
no code implementations • 28 May 2021 • Jia Yan, Suzhi Bi, Ying-Jun Angela Zhang
In practice, the DNN model is re-trained and updated periodically at the edge server.
no code implementations • 19 May 2021 • Zheyuan Yang, Suzhi Bi, Ying-Jun Angela Zhang
In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals.
no code implementations • 3 Mar 2021 • Dian Fan, Xiaojun Yuan, Ying-Jun Angela Zhang
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system.
no code implementations • 22 Feb 2021 • Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang
We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable.
no code implementations • 21 Jan 2021 • Tong Wu, Changhong Zhao, Ying-Jun Angela Zhang
In this way, the dual update of ADMM can be encrypted by PHE.
no code implementations • 18 Jan 2021 • Zhen-Qing He, Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang, Ying-Chang Liang
In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the passive RIS elements.
Bayesian Inference Information Theory Information Theory
1 code implementation • 20 Nov 2020 • Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang
However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance.
1 code implementation • 3 Oct 2020 • Suzhi Bi, Liang Huang, Hui Wang, Ying-Jun Angela Zhang
In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints.
Edge-computing Networking and Internet Architecture
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 • 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.
4 code implementations • 6 Aug 2018 • Liang Huang, Suzhi Bi, Ying-Jun Angela Zhang
To tackle this problem, we propose in this paper a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network to generate offloading decisions.
Networking and Internet Architecture