100 citations · 234 across the 21 of their papers we have counts for
10 papers · 1 filter
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
Qiong Wu, Xu Chen, Zhi Zhou +2
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and cap…
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
Qiong Wu, Xu Chen, Zhi Zhou +1
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and rec…
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
Liekang Zeng, Xu Chen, Zhi Zhou +2
Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computa…
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Shuai Yu, Xu Chen, Zhi Zhou +2
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilizati…
Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing
Huirong Ma, Zhi Zhou, Xu Chen
Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the chall…
Age of Processing: Age-driven Status Sampling and Processing Offloading for Edge Computing-enabled Real-time IoT Applications
Rui Li, Qian Ma, Jie Gong +2
The freshness of status information is of great importance for time-critical Internet of Things (IoT) applications. A metric measuring status freshness is the age-of-information (A…
HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
Deyin Liu, Xu Chen, Zhi Zhou +1
Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI applications. Conventional approaches to training DNNs are generally implemented at central se…
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
En Li, Liekang Zeng, Zhi Zhou +1
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challen…
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing
Tao Ouyang, Zhi Zhou, Xu Chen
Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of compu…