2 citations · 4 across the 11 of their papers we have counts for
13 papers
CoRedSkill: Stealthy Skill Composition Attacks via AST-Guided Injection
Qichen Zhao, Shengfang Zhai, Qingni Shen +2
Agents extend their capabilities through reusable skills that package instructions, executable code, and resources. Composing these skills enables complex task workflows, but their…
ACLE-MCP: Attested Capability Leases for Execution-Time Trust in Remote LLM Tool Use
Zhiyang Ding, Yang Luo, Guangpu Chen +2
Remote Model Context Protocol (MCP) services enable large language model agents to invoke external tools, but OAuth authorization alone does not ensure that a later tool call is ex…
JITterFlip: Uncovering Fault Attack Surfaces in JIT-Compiled LLM Serving
Tairui Wang, Zhi Zhang, Yansong Gao +3
LLMs are widely deployed through cloud-hosted inference services, where Just-in-Time (JIT) compilation is used to reduce recurring framework and GPU-launch overhead. JIT serving in…
Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch
Qichen Zhao, Shengfang Zhai, Xinjian Bai +4
Diffusion models enable high-fidelity image editing but can also be misused for unauthorized style imitation and harmful content generation. To mitigate these risks, proactive imag…
Exploring Information Seeking Agent Consolidation
Guochen Yan, Jialong Wu, Zhengwei Tao +8
Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today's systems remain specialized for the open web, documents, or local knowledge…
FedSRD: Sparsify-Reconstruct-Decompose for Communication-Efficient Federated Large Language Models Fine-Tuning
Guochen Yan, Luyuan Xie, Qingni Shen +2
The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaus…
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
Luyuan Xie, Tianyu Luan, Wenyuan Cai +7
Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, exist…
SoK: Understanding zk-SNARKs: The Gap Between Research and Practice
Junkai Liang, Daqi Hu, Pengfei Wu +3
Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are a powerful tool for proving computation correctness, attracting significant interest from researchers…
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Guochen Yan, Luyuan Xie, Xinyi Gao +4
Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distr…
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
Guochen Yan, Xunkai Li, Luyuan Xie +3
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distribu…
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
Luyuan Xie, Manqing Lin, ChenMing Xu +7
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates colla…
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
Luyuan Xie, Manqing Lin, Siyuan Liu +6
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity an…
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Luyuan Xie, Manqing Lin, Tianyu Luan +4
Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network arch…