85 citations · 141 across the 11 of their papers we have counts for
11 papers
StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
Tengjun Ni, Xin Yuan, Shenghong Li +4
Recent progress in retrieval-augmented generation (RAG) has led to more accurate and interpretable multi-hop question answering (QA). Yet, challenges persist in integrating iterati…
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…
A Secure Aggregation for Federated Learning on Long-Tailed Data
Yanna Jiang, Baihe Ma, Xu Wang +4
As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. I…
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…
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.…