23 citations · 39 across the 10 of their papers we have counts for
10 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…
Input Reconstruction Attack against Vertical Federated Large Language Models
Fei Zheng
Recently, large language models (LLMs) have drawn extensive attention from academia and the public, due to the advent of the ChatGPT. While LLMs show their astonishing ability in t…
Defending Label Inference Attacks in Split Learning under Regression Setting
Haoze Qiu, Fei Zheng, Chaochao Chen +1
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched. However, numerous studies have indicated that the priva…
Federated Learning on Non-iid Data via Local and Global Distillation
Xiaolin Zheng, Senci Ying, Fei Zheng +4
Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of…
Reducing Communication for Split Learning by Randomized Top-k Sparsification
Fei Zheng, Chaochao Chen, Lingjuan Lyu +1
Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficien…
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…
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Fei Zheng, Chaochao Chen, Xiaolin Zheng +1
With the increasing demands for privacy protection, privacy-preserving machine learning has been drawing much attention in both academia and industry. However, most existing method…