10 citations · 21 across the 7 of their papers we have counts for
7 papers
The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective
Cheng Wang, Zenghui Yuan, Pan Zhou +3
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. By shifting the…
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization
Wenbo Gou, Wen Shi, Jian Lou +3
Natural language video localization (NLVL) is an important task in the vision-language understanding area, which calls for an in-depth understanding of not only computer vision and…
Chunk Content is not Enough: Chunk-Context Aware Resemblance Detection for Deduplication Delta Compression
Xuming Ye, Xiaoye Xue, Wenlong Tian +3
With the growing popularity of cloud storage, removing duplicated data across users is getting more critical for service providers to reduce costs. Recently, Data resemblance detec…
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
Haozhao Wang, Zhihao Qu, Song Guo +3
Federated Learning is a powerful machine learning paradigm to cooperatively train a global model with highly distributed data. A major bottleneck on the performance of distributed…
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Chengjie Li, Ruixuan Li, Haozhao Wang +4
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are dist…
Heterogeneity-aware Gradient Coding for Straggler Tolerance
Haozhao Wang, Song Guo, Bin Tang +2
Gradient descent algorithms are widely used in machine learning. In order to deal with huge volume of data, we consider the implementation of gradient descent algorithms in a distr…
AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Neural Networks
Jinrong Guo, Wantao Liu, Wang Wang +4
Typically, Ultra-deep neural network(UDNN) tends to yield high-quality model, but its training process is usually resource intensive and time-consuming. Modern GPU's scarce DRAM ca…