313 citations · 493 across the 14 of their papers we have counts for
14 papers
Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach
Jinghong Tan, Zhian Liu, Kun Guo +1
Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Intern…
The Potential of LEO Satellites in 6G Space-Air-Ground Enabled Access Networks
Ziye Jia, Chao Dong, Kun Guo +1
Space-air-ground integrated networks (SAGINs) help enhance the service performance in the sixth generation communication system. SAGIN is basically composed of satellites, aerial v…
Energy-Efficient URLLC Service Provision via a Near-Space Information Network
Puguang An, Peng Yang, Xianbin Cao +3
The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of…
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…
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Chaoqun You, Kun Guo, Howard H. Yang +1
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in m…
MDC Enhanced IoT Networks: Network Modeling and Performance Analysis
Hongguang Sun, Yajun Ma, Tony Q. S. Quek +3
As a promising architecture, Mobile Data Collector (MDC) enhanced Internet of Things (IoT) exhibits broad prospects in efficient data collection and data aggregation especially for…
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…
Towards Effective Resource Procurement in MEC: a Resource Re-selling Framework
Marie Siew, Shikhar Sharma, Kun Guo +4
On-demand and resource reservation pricing models have been widely used in cloud computing, catering to different user requirements. Nevertheless, in Multi-Access Edge Computing (M…
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Peng Wei, Kun Guo, Ye Li +5
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertaint…
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…
Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network
Peng Yang, Kun Guo, Xing Xi +3
In this paper, we investigate a private and cache-enabled unmanned aerial vehicle (UAV) network for content provision. Aiming at delivering fresh, fair, and energy-efficient conten…
Let's Share VMs: Optimal Placement and Pricing across Base Stations in MEC Systems
Marie Siew, Kun Guo, Desmond Cai +2
In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might…
Multi-Armed Bandit Based Client Scheduling for Federated Learning
Wenchao Xia, Tony Q. S. Quek, Kun Guo +3
By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and pres…