85 citations · 227 across the 12 of their papers we have counts for
14 papers
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…
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Zihao Zhou, Yanan Li, Xuebin Ren +1
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considerin…
Latent Dirichlet Allocation Model Training with Differential Privacy
Fangyuan Zhao, Xuebin Ren, Shusen Yang +3
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning
Yuanrui Dong, Peng Zhao, Hanqiao Yu +2
The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, respons…
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…