5 papers
Rethinking Large Language Models For Irregular Time Series Classification In Critical Care
Feixiang Zheng, Yu Wu, Cecilia Mascolo +1
Time series data from the Intensive Care Unit (ICU) provides critical information for patient monitoring. While recent advancements in applying Large Language Models (LLMs) to time…
PADER: Paillier-based Secure Decentralized Social Recommendation
Chaochao Chen, Jiaming Qian, Fei Zheng +1
The prevalence of recommendation systems also brings privacy concerns to both the users and the sellers, as centralized platforms collect as much data as possible from them. To kee…
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
PermLLM: Private Inference of Large Language Models within 3 Seconds under WAN
Fei Zheng, Chaochao Chen, Zhongxuan Han +1
The emergence of ChatGPT marks the arrival of the large language model (LLM) era. While LLMs demonstrate their power in a variety of fields, they also raise serious privacy concern…
Protecting Split Learning by Potential Energy Loss
Fei Zheng, Chaochao Chen, Lingjuan Lyu +5
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the…