12 papers
Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
Zikai Zhang, Rui Hu, Olivera Kotevska +1
Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate…
SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits
Zikai Zhang, Rui Hu, Olivera Kotevska +1
Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing guardrail methods typically rely on int…
Majority Bit-Aware Watermarking For Large Language Models
Jiahao Xu, Rui Hu, Olivera Kotevska +1
The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To address this issue, watermarking method…
XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts
Jiahao Xu, Rui Hu, Olivera Kotevska +1
Multi-bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)-generated text, enabling reliable attribution…
Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation
Zikai Zhang, Rui Hu, Jiahao Xu
Large Language Models (LLMs) have demonstrated remarkable effectiveness in adapting to downstream tasks through fine-tuning. Federated Learning (FL) extends this capability by enab…
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Jiahao Xu, Rui Hu, Olivera Kotevska
Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. Howe…