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

Last update: Jan 08, 2023

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Deep Learning LDP-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]

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

Related tags

Overview

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

Requirements

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

Owner

Di Zhuang

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

Related tags

Overview

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

Requirements

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

Owner

Di Zhuang

This repository contains the code for: RerrFact model for SciVer shared task

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[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation

EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos.

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