294 citations · 589 across the 24 of their papers we have counts for
24 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…
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…
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…
Exploring the Benefits of Visual Prompting in Differential Privacy
Yizhe Li, Yu-Lin Tsai, Xuebin Ren +2
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this w…
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…
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…
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…
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…
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Jun Zhao, Teng Wang, Tao Bai +7
Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…
On Privacy Protection of Latent Dirichlet Allocation Model Training
Fangyuan Zhao, Xuebin Ren, Shusen Yang +1
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine…
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Yanan Li, Xuebin Ren, Shusen Yang +1
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate…
Locally Differentially Private Data Collection and Analysis
Teng Wang, Jun Zhao, Xinyu Yang +1
Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data.…
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Teng Wang, Jun Zhao, Han Yu +4
With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dil…
LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy
Xuebin Ren, Chia-Mu Yu, Weiren Yu +4
High-dimensional crowdsourced data collected from a large number of users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to particip…