1 citations · 1 across the 13 of their papers we have counts for
13 papers
When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems
Chenqing Zhu, Yanbo Dai, Yulong Tian +2
Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counseling, and legal consultation…
PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
Mingxuan Zhang, Jiahui Han, Dadi Guo +5
LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users. However, agents often acquire more sensitive information than t…
Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models
Kai Wang, Jiale Zhang, Chengcheng Zhu +2
Text-to-image diffusion models are increasingly developed through open-source reuse and repeated downstream fine-tuning, where reused checkpoints are difficult to verify and thus m…
When Efficiency Backfires: Cascading LLMs Trigger Cascade Failure under Adversarial Attack
Zehan Sun, Dingfan Chen, Songze Li
Large Language Model (LLM) cascade systems are designed to balance efficiency and performance by processing queries with lightweight models while selectively escalating complex cas…
Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice
Jiahao Chen, Zhiming Zhao, Yuwen Pu +4
Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly understood. In particular, a large body…
Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models
Sizai Hou, Songze Li, Baturalp Buyukates
Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (FPP) is further developed to add…
TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data
Yanbo Dai, Songze Li, Zihan Gan +1
Federated learning (FL) systems allow decentralized data-owning clients to jointly train a global model through uploading their locally trained updates to a centralized server. The…
Hidden Data Privacy Breaches in Federated Learning
Xueluan Gong, Yuji Wang, Shuaike Li +5
Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct d…
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders
Sizai Hou, Songze Li, Duanyi Yao
Self-supervised learning (SSL) is pervasively exploited in training high-quality upstream encoders with a large amount of unlabeled data. However, it is found to be susceptible to…
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
Duanyi Yao, Songze Li, Ye Xue +1
Vertical federated learning (VFL), where each participating client holds a subset of data features, has found numerous applications in finance, healthcare, and IoT systems. However…
PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
Sizai Hou, Songze Li, Tayyebeh Jahani-Nezhad +1
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typi…
BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning
Songze Li, Yanbo Dai
In a federated learning (FL) system, decentralized data owners (clients) could upload their locally trained models to a central server, to jointly train a global model. Malicious c…
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
Duanyi Yao, Songze Li, Xueluan Gong +2
Launching effective malicious attacks in VFL presents unique challenges: 1) Firstly, given the distributed nature of clients' data features and models, each client rigorously guard…