85 citations · 173 across the 14 of their papers we have counts for
14 papers
Leverage Variational Graph Representation For Model Poisoning on Federated Learning
Kai Li, Xin Yuan, Jingjing Zheng +3
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder…
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
Siguo Bi, Xin Yuan, Shuyan Hu +4
The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achiev…
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
Kai Li, Jingjing Zheng, Xin Yuan +3
This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack re…
OFDMA-FL: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Shuyan Hu, Xin Yuan, Wei Ni +3
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of prac…
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
Kai Li, Billy Pik Lik Lau, Xin Yuan +3
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, whic…
New Adversarial Image Detection Based on Sentiment Analysis
Yulong Wang, Tianxiang Li, Shenghong Li +2
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e.g., DeepFool, are on the rise and outrunning adversarial example detection te…
Learn to Unlearn: A Survey on Machine Unlearning
Youyang Qu, Xin Yuan, Ming Ding +3
Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on re…
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Yulong Wang, Tong Sun, Shenghong Li +4
Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the…
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
Xin Yuan, Wei Ni, Ming Ding +3
While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP no…
RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework
Shuyan Hu, Xin Yuan, Wei Ni +2
This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-…
Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems
Xin Yuan, Shuyan Hu, Wei Ni +2
Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, mod…
When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds
Kai Li, Yingping Cui, Weicai Li +6
In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This su…
Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models
Yulong Wang, Minghui Zhao, Shenghong Li +2
Typical deep neural network (DNN) backdoor attacks are based on triggers embedded in inputs. Existing imperceptible triggers are computationally expensive or low in attack success.…
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
Changhong Fu, Sihang Li, Xinnan Yuan +3
Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related applications, which leads to a highly demanding requirement on the robustness of UAV trackers. However…