294 citations · 573 across the 32 of their papers we have counts for
7 papers · 1 filter
Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation
Hairong Su, Shibo Wang, Shusen Yang +2
Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer f…
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…
Review of Mathematical Optimization in Federated Learning
Shusen Yang, Fangyuan Zhao, Zihao Zhou +3
Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively…
Understanding Byzantine Robustness in Federated Learning with A Black-box Server
Fangyuan Zhao, Yuexiang Xie, Xuebin Ren +3
Federated learning (FL) becomes vulnerable to Byzantine attacks where some of participators tend to damage the utility or discourage the convergence of the learned model via sendin…
VertiMRF: Differentially Private Vertical Federated Data Synthesis
Fangyuan Zhao, Zitao Li, Xuebin Ren +3
Data synthesis is a promising solution to share data for various downstream analytic tasks without exposing raw data. However, without a theoretical privacy guarantee, a synthetic…
EdgeTimer: Adaptive Multi-Timescale Scheduling in Mobile Edge Computing with Deep Reinforcement Learning
Yijun Hao, Shusen Yang, Fang Li +3
In mobile edge computing (MEC), resource scheduling is crucial to task requests' performance and service providers' cost, involving multi-layer heterogeneous scheduling decisions.…
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…