2 citations · 3 across the 7 of their papers we have counts for
7 papers
Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
Yong Chu, Xun Zhou, Zenglin Xu +2
Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing. Despite it…
SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC
Jinglong Luo, Zhuo Zhang, Yehong Zhang +6
Large Language Models (LLMs) have revolutionized numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains such as healthcare and finance remains cons…
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
Jinglong Luo, Guanzhong Chen, Yehong Zhang +6
With the growing deployment of pre-trained models like Transformers on cloud platforms, privacy concerns about model parameters and inference data are intensifying. Existing Privac…
Training and Serving System of Foundation Models: A Comprehensive Survey
Jiahang Zhou, Yanyu Chen, Zicong Hong +6
Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-) have demonstrated extraordinary performance in key technological areas, such as natural language processing and…
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
Jinglong Luo, Yehong Zhang, Zhuo Zhang +5
With the growing use of Transformer models hosted on cloud platforms to offer inference services, privacy concerns are escalating, especially concerning sensitive data like investm…
EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection
Xin Mu, Yu Wang, Zhengan Huang +4
In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learn…
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
Zhuo Zhang, Yuanhang Yang, Yong Dai +2
With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks.…