Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL)

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Deep LearningLDP-DL
Overview

Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL)

  • A preprint version of our paper: Link here

  • This is a sample code of our LDP-DL paper, including all the necessary implementations: 1) DataPartation.py: split the data for data owners and data user; 2) TeacherModel.py: train the teacher models using data owners' private data; 3) StudentModel_AQS.py: train the student model via active query sampling and knowledge distillation using data user's public data.

  • The splited data and trained teacher models are also provided. Due to the limitation of capacity, please download from this link:

Requirements

  • Pytorch 1.4.0, numpy 1.18.1, scipy 1.4.1, cuda 10.0, joblib 0.14.1

Quick Start (only tested using PyCharm on Ubuntu, Python 3.7)

  • Download or clone the whole repository.
  • Import our code as a project into PyCharm.
  • Run DataPartation.py to split the data.
  • Run TeacherModel.py to train teacher models.
  • Run StudentModle_AQS.py to train and evaluate student model.

Contact info: [email protected], [email protected]

Owner
Di Zhuang
Di Zhuang
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