2 citations · 3 across the 10 of their papers we have counts for
10 papers
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
Xiaoyu Luo, Tao Ren, Wenrui Yu +3
The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT traces in closed-source systems…
When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning
Wenrui Yu, Changlong Ji, Johannes Bjerva +1
Secure Aggregation (SA) is widely regarded as a strong defense against model-update leakage in Federated Learning (FL), as it reveals only aggregate results while hiding individual…
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 full local corpora remain on clients. As a result, routing must rely on c…
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…
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…
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…
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study
Changlong Ji, Stephane Maag, Richard Heusdens +1
Decentralized Federated Learning (DFL) has garnered attention for its robustness and scalability compared to Centralized Federated Learning (CFL). While DFL is commonly believed to…
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
Qiongxiu Li, Lixia Luo, Agnese Gini +6
Federated Learning (FL) has emerged as a popular paradigm for collaborative learning among multiple parties. It is considered privacy-friendly because local data remains on persona…
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…