359 citations · 776 across the 7 of their papers we have counts for
7 papers
PDMA: Probabilistic Service Migration Approach for Delay-aware and Mobility-aware Mobile Edge Computing
Minxian Xu, Qiheng Zhou, Huaming Wu +3
As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the pr…
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
Tiansheng Huang, Weiwei Lin, Xiaobin Hong +5
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet…
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang, Weiwei Lin, Li Shen +2
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a…
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
Tiansheng Huang, Weiwei Lin, Wentai Wu +3
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, ter…
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
Wentai Wu, Ligang He, Weiwei Lin +1
Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile,…
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Wentai Wu, Ligang He, Weiwei Lin +3
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challen…
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
Wentai Wu, Ligang He, Weiwei Lin +4
On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center…