294 citations · 573 across the 32 of their papers we have counts for
33 papers
Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus
Hanqiao Yu, Shusen Yang, Xuebin Ren +1
A fundamental problem in science is identifying underlying patterns of complex systems in the form of concise mathematical formulas. Current Artificial Intelligence (AI)-based meth…
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
Zihao Zhou, Shusen Yang, Fangyuan Zhao +1
Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. However, graph data oft…
Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Qifeng Tan, Shusen Yang, Xuebin Ren +1
Layer-wise Gaussian mechanisms (LGM) enhance flexibility in differentially private deep learning by injecting noise into partitioned gradient vectors. However, existing methods oft…
Generative Model-Based Feature Attention Module for Video Action Analysis
Guiqin Wang, Peng Zhao, Cong Zhao +3
Video action analysis is a foundational technology within the realm of intelligent video comprehension, particularly concerning its application in Internet of Things(IoT). However,…
Fine-grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams
Xinyu Li, Xuebin Ren, Shusen Yang +2
Local Differential Privacy (LDP) enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data…
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…
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…
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…
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…
DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data
Xuebin Ren, Chia-Mu Yu, Wei Yu +3
In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a l…
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…
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…