31 citations · 57 across the 6 of their papers we have counts for
6 papers
A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakage
Huan Yang, Deyu Zhang, Yudong Zhao +2
Running LLMs on end devices has garnered significant attention recently due to their advantages in privacy preservation. With the advent of lightweight LLM models and specially des…
LLM as a System Service on Mobile Devices
Wangsong Yin, Mengwei Xu, Yuanchun Li +1
Being more powerful and intrusive into user-device interactions, LLMs are eager for on-device execution to better preserve user privacy. In this work, we propose a new paradigm of…
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Yuanchun Li, Hao Wen, Weijun Wang +22
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming…
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
Yizhen Yuan, Rui Kong, Shenghao Xie +2
Backdoor attack is a major threat to deep learning systems in safety-critical scenarios, which aims to trigger misbehavior of neural network models under attacker-controlled condit…
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments
Hao Wen, Yuanchun Li, Zunshuai Zhang +5
Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable…
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin, Yuanchun Li, Yunxin Liu +1
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-orga…