8 citations · 17 across the 15 of their papers we have counts for
15 papers
Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning
Naicheng Li, Javad Dogani, Rui Wang +2
Traditional federated learning (FL) relies on a central aggregator server, which can create performance bottlenecks and privacy risks. Decentralized mix-and-forward designs remove…
ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography
Yaofei Wang, Rui Wang, Weilong Pang +4
Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however,…
DETOUR: A Practical Backdoor Attack against Object Detection
Dazhuang Liu, Yanqi Qiao, Rui Wang +2
Object detection (OD) is critical to real-world vision systems, yet existing backdoor attacks on detection transformers (DETRs) for OD tasks rely on patch-wise triggers optimized a…
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
Zihan Liu, Yizhen Wang, Rui Wang +2
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. Wh…
PASTA: A Patch-Agnostic Twofold-Stealthy Backdoor Attack on Vision Transformers
Dazhuang Liu, Yanqi Qiao, Rui Wang +2
Vision Transformers (ViTs) have achieved remarkable success across vision tasks, yet recent studies show they remain vulnerable to backdoor attacks. Existing patch-wise attacks typ…
Graph Federated Learning Based Proactive Content Caching in Edge Computing
Rui Wang
With the rapid growth of mobile data traffic and the increasing prevalence of video streaming, proactive content caching in edge computing has become crucial for reducing latency a…
LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm
Dazhuang Liu, Yanqi Qiao, Rui Wang +2
Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as single-objective optimization problems in single domain. Designing triggers in…
Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm
Yanqi Qiao, Dazhuang Liu, Rui Wang +1
While convolutional neural networks (CNNs) have achieved success in computer vision tasks, it is vulnerable to backdoor attacks. Such attacks could mislead the victim model to make…
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning
Yanqi Qiao, Dazhuang Liu, Congwen Chen +2
Current backdoor attacks against federated learning (FL) strongly rely on universal triggers or semantic patterns, which can be easily detected and filtered by certain defense mech…
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
Rui Wang, Xingkai Wang, Huanhuan Chen +4
Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing…
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks
Ignjat Pejic, Rui Wang, Kaitai Liang
A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Machine Learning (ML) that involves training two Neural Networks (NN) using a sizable dat…
FLVoogd: Robust And Privacy Preserving Federated Learning
Yuhang Tian, Rui Wang, Yanqi Qiao +2
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particu…
Using Autoencoders on Differentially Private Federated Learning GANs
Gregor Schram, Rui Wang, Kaitai Liang
Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical resear…
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Jing Xu, Rui Wang, Stefanos Koffas +2
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due t…
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Hangyu Zhu, Rui Wang, Yaochu Jin +1
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing th…