Search Results for author:
Found 7 papers, 1 papers with code
Input Reconstruction Attack against Vertical Federated Large Language Models
However, in this paper, we demonstrate that in LLMs, VFL fails to protect the user input since it is simple and cheap to reconstruct the input from the intermediate embeddings.
Defending Label Inference Attacks in Split Learning under Regression Setting
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched.
Federated Learning on Non-iid Data via Local and Global Distillation
In this work, we propose FedND: federated learning with noise distillation.
Reducing Communication for Split Learning by Randomized Top-k Sparsification
However, communication efficiency is still a crucial issue for split learning.
Making Split Learning Resilient to Label Leakage by Potential Energy Loss
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
Efficient Private Machine Learning by Differentiable Random Transformations
With the increasing demands for privacy protection, many privacy-preserving machine learning systems were proposed in recent years.
