4 citations · 5 across the 7 of their papers we have counts for
7 papers
Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Yueyang Quan, Chang Wang, Shengjie Zhai +2
Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neig…
VReaves: Eavesdropping on Virtual Reality App Identity and Activity via Electromagnetic Side Channels
Wei Sun, Minghong Fang, Mengyuan Li
Virtual reality (VR) has recently proliferated significantly, consisting of headsets or head-mounted displays (HMDs) and hand controllers for an embodied and immersive experience.…
Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Jiatong Li +5
Retrieval-Augmented Generation (RAG) has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this…
Byzantine-Robust Decentralized Federated Learning
Minghong Fang, Zifan Zhang, Hairi +5
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system foll…
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu, Ming Yin, Minghong Fang +1
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as cl…
Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang +1
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attac…