research
∙
09/16/2023
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
Web applications are increasingly becoming the primary platform for AI s...
share
research
∙
09/07/2023
Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains
Recently unsupervised machine learning based systems have been developed...
research
∙
03/27/2023
PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels
Freshness-aware computation offloading has garnered great attention rece...
research
∙
02/09/2023
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
This work aims to tackle a major challenge in offline Inverse Reinforcem...
research
∙
08/14/2021
Efficient Federated Meta-Learning over Multi-Access Wireless Networks
Federated meta-learning (FML) has emerged as a promising paradigm to cop...
research
∙
01/07/2021
TODG: Distributed Task Offloading with Delay Guarantees for Edge Computing
Edge computing has been an efficient way to provide prompt and near-data...
research
∙
12/16/2020
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
In order to meet the requirements for performance, safety, and latency i...
research
∙
05/19/2019
Learning-Based Priority Pricing for Job Offloading in Mobile Edge Computing
Mobile edge computing (MEC) is an emerging paradigm where users offload ...
research
∙
03/15/2019
Cloud-Edge Coordinated Processing: Low-Latency Multicasting Transmission
Recently, edge caching and multicasting arise as two promising technolog...
research
∙
01/22/2019
Hybrid Precoder Design for Cache-enabled Millimeter Wave Radio Access Networks
In this paper, we study the design of a hybrid precoder, consisting of a...
research
∙
12/14/2018
