2 citations · 2 across the 4 of their papers we have counts for
8 papers
The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?
Xingyu Lyu, Qianqian Xu, Zhiyong Yang +2
Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform their non-gated counterparts, ye…
ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation
Xingyu Lyu, Jianfeng He, Ning Wang +5
Retrieval-Augmented Generation (RAG) is widely used to augment large language models with external knowledge retrieval to improve reliability and generalization. However, recent st…
ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying
Xingyu Lyu, Jianfeng He, Ning Wang +5
Large Language Model (LLM) agents have achieved rapid adoption and demonstrated remarkable capabilities across a wide range of applications. To improve reasoning and task execution…
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
Zhengyuan Jiang, Xingyu Lyu, Shanghao Shi +5
Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have sho…
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
Xingyu Lyu, Ning Wang, Yang Xiao +4
Federated Learning (FL) is a popular paradigm enabling clients to jointly train a global model without sharing raw data. However, FL is known to be vulnerable towards backdoor atta…
Demystifying Private Transactions and Their Impact in PoW and PoS Ethereum
Xingyu Lyu, Mengya Zhang, Xiaokuan Zhang +3
In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the c…