7 citations · 28 across the 22 of their papers we have counts for
22 papers
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices
Guanyu Xu, Zhiwei Hao, Yong Luo +3
Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT mode…
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient
Rongfei Fan, Xuming An, Shiyuan Zuo +1
Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss funct…
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning
Xuming An, Rongfei Fan, Shiyuan Zuo +3
Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the trai…
Joint Task Offloading and Resource Allocation for Streaming Application in Cooperative Mobile Edge Computing
Xiang Li, Rongfei Fan, Han Hu +1
Mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or delay-sensitive task by offloading the task to adjacent edge server deployed a…
FedABC: Targeting Fair Competition in Personalized Federated Learning
Dui Wang, Li Shen, Yong Luo +4
Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poo…
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
Mengya Han, Yibing Zhan, Yong Luo +4
Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning pa…
Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning
Zhiwei Hao, Guanyu Xu, Yong Luo +3
Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge…
CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing
Zhiwei Hao, Yong Luo, Zhi Wang +2
Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Alth…
Intelligent Resource Allocations for IRS-Assisted OFDM Communications: A Hybrid MDQN-DDPG Approach
Wei Wu, Fengchun Yang, Fuhui Zhou +3
In this paper, we study the resource allocation problem for an intelligent reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization framework is formulated b…
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
Han Hu, Weiwei Song, Qun Wang +2
Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical app…
Joint Task Offloading and Resource Allocation for IoT Edge Computing with Sequential Task Dependency
Xuming An, Rongfei Fan, Han Hu +3
Incorporating mobile edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this p…
Towards a Systematic Survey for Carbon Neutral Data Centers
Zhiwei Cao, Xin Zhou, Han Hu +2
Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8\% of global carbon emissions…
Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA
Qun Wang, Fuhui Zhou, Han Hu +1
Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks. In order to serve massive users while achieving an energy-efficient operation, an in…
Multimedia Edge Computing
Zhi Wang, Wenwu Zhu, Lifeng Sun +6
In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals' geographical preference and…
Energy-efficient Task Offloading for Relay Aided Mobile Edge Computing under Sequential Task Dependency
Xiangg Li, Rongfei Fan, Han Hu
In this paper, we study a mobile edge computing (MEC) system in which the mobile device is assisted by a base station (BS) and a cooperative node. The mobile device has sequential…
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
Han Hu, Weiwei Song, Qun Wang +2
Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT device…
Energy-Efficient Task Offloading and Resource Allocation for Multiple Access Mobile Edge Computing
Bizheng Liang, Rongfei Fan, Han Hu
In this paper, the problem of joint radio and computation resource management over multi-channel is investigated for multi-user partial offloading mobile edge computing (MEC) syste…
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
Qun Wang, Han Hu, Haijian Sun +1
Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existi…
Joint Task Offloading and Resource Allocation for IoT Edge Computing with Sequential Task Dependency
Xuming An, Rongfei Fan, Han Hu +3
Incorporating mobile edge computing (MEC) in Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this paper…
Energy-efficient Resource Allocation for Mobile Edge Computing Aided by Multiple Relays
Xiang Li, Rongfei Fan, Han Hu +2
In this paper, we study a mobile edge computing (MEC) system with the mobile device aided by multiple relay nodes for offloading data to an edge server. Specifically, the modes of…
DeepQoE: A unified Framework for Learning to Predict Video QoE
Huaizheng Zhang, Han Hu, Guanyu Gao +2
Motivated by the prowess of deep learning (DL) based techniques in prediction, generalization, and representation learning, we develop a novel framework called DeepQoE to predict v…
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
Yuanlong Li, Han Hu, Yonggang Wen +1
In this paper, for the purpose of data centre energy consumption monitoring and analysis, we propose to detect the running programs in a server by classifying the observed power co…