85 citations · 130 across the 12 of their papers we have counts for
12 papers
TelOps: AI-driven Operations and Maintenance for Telecommunication Networks
Yuqian Yang, Shusen Yang, Cong Zhao +1
Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely importan…
Belt and Braces: When Federated Learning Meets Differential Privacy
Xuebin Ren, Shusen Yang, Cong Zhao +2
Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection…
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Jie Shen, Shusen Yang, Cong Zhao +5
Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial ag…
Generative Model-based Feature Knowledge Distillation for Action Recognition
Guiqin Wang, Peng Zhao, Yanjiang Shi +2
Knowledge distillation (KD), a technique widely employed in computer vision, has emerged as a de facto standard for improving the performance of small neural networks. However, pre…
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling
Guiqin Wang, Peng Zhao, Cong Zhao +5
Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple insta…
LDP-IDS: Local Differential Privacy for Infinite Data Streams
Xuebin Ren, Liang Shi, Weiren Yu +3
Streaming data collection is essential to real-time data analytics in various IoTs and mobile device-based systems, which, however, may expose end users' privacy. Local differentia…
ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
Luhui Wang, Cong Zhao, Shusen Yang +2
Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service late…
OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
Qing Han, Shusen Yang, Xuebin Ren +3
Distributed machine learning (ML) at network edge is a promising paradigm that can preserve both network bandwidth and privacy of data providers. However, heterogeneous and limited…
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning
Shibo Wang, Shusen Yang, Cong Zhao
The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditiona…
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Yanan Li, Shusen Yang, Xuebin Ren +1
Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in…
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Cong Zhao, Xinyu Yang, Wei Yu +3
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. A…
Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing
Cong Zhao, Shusen Yang, Xinyu Yang +1
Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing and collaborative computin…