activity
20202025
most citedMulti-Armed Bandit Based Client Scheduling for Federated Learning

313 citations · 493 across the 14 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.NI2022★ 5 cited

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…

cs.LG2022★ 27 cited

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…

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…

cs.NI2022

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…

cs.IT2022★ 73 cited

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…