2 citations · 4 across the 4 of their papers we have counts for
5 papers
UTune: Towards Uncertainty-Aware Online Index Tuning
Chenning Wu, Sifan Chen, Wentao Wu +4
There have been a flurry of recent proposals on learned benefit estimators for index tuning. Although these learned estimators show promising improvement over what-if query optimiz…
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Xi Li, Ruofan Mao, Yusen Zhang +3
Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often…
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Xi Li, Chen Wu, Jiaqi Wang
Federated Learning (FL), a privacy-preserving machine learning framework, faces significant data-related challenges. For example, the lack of suitable public datasets leads to inef…
Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning
Xi Li, Chen Wu, Jiaqi Wang
The foundation models (FMs) have been used to generate synthetic public datasets for the heterogeneous federated learning (HFL) problem where each client uses a unique model archit…
Backdoor Threats from Compromised Foundation Models to Federated Learning
Xi Li, Songhe Wang, Chen Wu +2
Federated learning (FL) represents a novel paradigm to machine learning, addressing critical issues related to data privacy and security, yet suffering from data insufficiency and…