2 citations · 4 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
Securing Federated Learning against Backdoor Threats with Foundation Model Integration
Xiaohuan Bi, Xi Li
Federated Learning (FL) enables decentralized model training while preserving privacy. Recently, the integration of Foundation Models (FMs) into FL has enhanced performance but int…
cs.CR2024★ 2 cited
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…
cs.CR2024★ 1 cited
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…