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20212026
most citedSemi-Synchronous Personalized Federated Learning over Mobile Edge Networks

27 citations · 51 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.NI2026

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…

cs.NI2026

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…

cs.NI2024★ 8 cited

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…

cs.NI2023★ 1 cited

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…

cs.NI2022★ 2 cited

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…

cs.NI2022★ 1 cited

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…