98 citations · 474 across the 39 of their papers we have counts for
22 papers · 1 filter
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium
Xinxin Fan, Wenxiong Chen, Mengfan Li +2
Adversarial attacks to graph analytics are gaining increased attention. To date, two lines of countermeasures have been proposed to resist various graph adversarial attacks from th…
On the Adversarial Robustness of Graph Neural Networks with Graph Reduction
Kerui Wu, Ka-Ho Chow, Wenqi Wei +1
As Graph Neural Networks (GNNs) become increasingly popular for learning from large-scale graph data across various domains, their susceptibility to adversarial attacks when using…
Data Poisoning and Leakage Analysis in Federated Learning
Wenqi Wei, Tiansheng Huang, Zachary Yahn +3
Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating…
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
Wenqi Wei, Ling Liu
Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have iden…
Hierarchical Pruning of Deep Ensembles with Focal Diversity
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing…
Rethinking Learning Rate Tuning in the Era of Large Language Models
Hongpeng Jin, Wenqi Wei, Xuyu Wang +2
Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverag…
Securing Distributed SGD against Gradient Leakage Threats
Wenqi Wei, Ling Liu, Jingya Zhou +2
This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). First, we analyze two types of strategies for privacy-enhanced…
GNN-Ensemble: Towards Random Decision Graph Neural Networks
Wenqi Wei, Mu Qiao, Divyesh Jadav
Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data. However, existing graph based applications commonly lack annotated data. GNNs are requi…
Machine Learning for Synthetic Data Generation: A Review
Yingzhou Lu, Lulu Chen, Yuanyuan Zhang +6
Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leadi…
Adaptive Deep Neural Network Inference Optimization with EENet
Fatih Ilhan, Ka-Ho Chow, Sihao Hu +9
Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test exampl…
Gradient Leakage Attack Resilient Deep Learning
Wenqi Wei, Ling Liu
Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromis…
Gradient-Leakage Resilient Federated Learning
Wenqi Wei, Ling Liu, Yanzhao Wu +2
Federated learning(FL) is an emerging distributed learning paradigm with default client privacy because clients can keep sensitive data on their devices and only share local traini…
Promoting High Diversity Ensemble Learning with EnsembleBench
Yanzhao Wu, Ling Liu, Zhongwei Xie +3
Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accu…
Robust Deep Learning Ensemble against Deception
Wenqi Wei, Ling Liu
Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the train…
LDP-Fed: Federated Learning with Local Differential Privacy
Stacey Truex, Ling Liu, Ka-Ho Chow +2
This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primaril…
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Wenqi Wei, Ling Liu, Margaret Loper +4
Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client pr…
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy +3
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of t…
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Wenqi Wei, Ling Liu, Margaret Loper +4
Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adv…
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
Ling Liu, Wenqi Wei, Ka-Ho Chow +4
Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the en…
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Ka-Ho Chow, Wenqi Wei, Yanzhao Wu +1
Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by add…
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Yanzhao Wu, Ling Liu, Juhyun Bae +6
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivi…
Adversarial Examples in Deep Learning: Characterization and Divergence
Wenqi Wei, Ling Liu, Margaret Loper +4
The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learnin…