6 papers
A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG
Junjie Mu, Qiongxiu Li
Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because raw data remain local. As a result, routing must rely on client-provided…
Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models
Xiao Li, Wei Zhang, Zhuhong Li +6
Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks that attempt to bypass guardrails via a…
SOMP: Scalable Gradient Inversion for Large Language Models via Subspace-Guided Orthogonal Matching Pursuit
Yibo Li, Qiongxiu Li
Gradient inversion attacks reveal that private training text can be reconstructed from shared gradients, posing a privacy risk to large language models (LLMs). While prior methods…
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
Xiao Li, Wenxuan Sun, Huanran Chen +5
Recently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs t…
Byzantine-Resilient Federated Learning via Distributed Optimization
Yufei Xia, Wenrui Yu, Qiongxiu Li
Byzantine attacks present a critical challenge to Federated Learning (FL), where malicious participants can disrupt the training process, degrade model accuracy, and compromise sys…
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
Qiongxiu Li, Wenrui Yu, Yufei Xia +1
Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized…