27 citations · 51 across the 11 of their papers we have counts for
12 papers
Online Adaptive Computation Reuse in Collaborative Edge Computing: A Two-Timescale Approach
Xingqiu He, Chaoqun You, Peng Wang +2
Collaborative Edge Computing (CEC) is an efficient computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhan…
Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks
Xingqiu He, Chaoqun You, Yuzhi Yang +4
Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enha…
Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?
Kun Guo, Yun Shen, Xijun Wang +3
Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent adv…
Lightweight Federated Learning in Mobile Edge Computing with Statistical and Device Heterogeneity Awareness
Jinghong Tan, Zhichen Zhang, Kun Guo +2
Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterog…
Exploiting Storage for Computing: Computation Reuse in Collaborative Edge Computing
Xingqiu He, Chaoqun You, Tony Q. S. Quek
Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance…
Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks
Xingqiu He, Chaoqun You, Zihan Chen +4
Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing…
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Xingqiu He, Chaoqun You, Tony Q. S. Quek
With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications…
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
Chaoqun You, Kun Guo, Gang Feng +2
Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a Model-Agnostic Meta…
Social Welfare Maximization for Collaborative Edge Computing: A Deep Reinforcement Learning-Based Approach
Xingqiu He, Yuhang Shen, Hongxi Zhu +3
Collaborative Edge Computing (CEC) is an effective method that improves the performance of Mobile Edge Computing (MEC) systems by offloading computation tasks from busy edge server…
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Chaoqun You, Daquan Feng, Kun Guo +2
Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). H…
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
Marie Siew, Shikhar Sharma, Zekai Li +5
In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. H…
Joint Network Topology Inference via Structured Fusion Regularization
Yanli Yuan, De Wen Soh, Xiao Yang +2
Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely…