11 citations · 11 across the 4 of their papers we have counts for
4 papers
Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directions
Lele Zheng, Tong Chen, Ke Cheng +5
Large-model-driven embodied agents integrate foundation models with perception, reasoning, planning, and physical action, extending conventional model-level risks into embodied clo…
When Convenience Becomes Risk: A Semantic View of Under-Specification in Host-Acting Agents
Di Lu, Yongzhi Liao, Xutong Mu +5
Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a di…
PriFFT: Privacy-preserving Federated Fine-tuning of Large Language Models via Hybrid Secret Sharing
Zhichao You, Xuewen Dong, Ke Cheng +5
Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping trainin…
FedProc: Prototypical Contrastive Federated Learning on Non-IID data
Xutong Mu, Yulong Shen, Ke Cheng +4
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of al…