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📋 A template README.md for code accompanying a Machine Learning paper

VICEROY

This repository is the official implementation of VICEROY.

📋 Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials

Todo

  • Work on Github Repo
  • Implement Client worker Code
  • Include PCA
  • Include differential privacy
  • Implement VFL concept

Environment Setup

Install Visual Studitio Code before setting up the environment

Vscode link

Install Docker

Docker link

Framework and Used Library

  • Develope environment : Docker

  • Languaage and version : Python 3.9

  • FL framework : Pysyft

  • FL client (worker) : syft.js

  • VFL library : PyVertical

To install requirements:

Step 0 Create an empty Folder call VFL and open it with Visual Studio Code

Step 1 Clone the repository

git clone https://github.com/AlexTsai1618/VICEROY.git

Step 2 launch docker

Step 3 Launch devcontainer

  • Step 3-1 Go to the bottom left side and click it

  • Step 3-2 Select the Reopen in Container to launch the devcontainer

  • Result Check whether the devcontainer is successfully

📋 Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...

Training

To train the model(s) in the paper, run this command:

python train.py --input-data <path_to_data> --alpha 10 --beta 20

📋 Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters.

Evaluation

To evaluate my model on ImageNet, run:

python eval.py --model-file mymodel.pth --benchmark imagenet

📋 Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).

Pre-trained Models

You can download pretrained models here:

📋 Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable). Alternatively you can have an additional column in your results table with a link to the models.

Results

Our model achieves the following performance on :

Image Classification on ImageNet

Model name Top 1 Accuracy Top 5 Accuracy
My awesome model 85% 95%

📋 Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it.

Contributing

📋 Pick a licence and describe how to contribute to your code repository.