Image-Classification-by-Federated-Learning

Project aimed to provide insights of the implementation of Fedarated Learning.

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Description

• Implementation of Federated Learning gist of which are:

  • Train your model without uploading client's data on cloud
  • Provides assurance of privacy in terms of interests and activity
  • Model hosted on cloud only uplaods the results back to the server
  • Thus, full-flegded security and assurance of none sort of fetching data/leakage.
  • More Details on Federated Learning : https://federated.withgoogle.com/

• Uses MNIST dataset of different writing styles considering them as data of various client's.

• Train model on federated data; predicting and customizing model implementation for accurate results.

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Dependencies

• tensorflow_federated_nightly also bring in tf_nightly, which can causes a duplicate tensorboard install, leading to errors.
• Follow the commands below to set up your environment correctly
  • pip uninstall --yes tensorboard tb-nightly removing previous versions to avoid duplicate installations
  • pip install --quiet --upgrade tensorflow-federated-nightly upgrading the package
  • pip install --quiet --upgrade nest-asyncio installing/upgrading other dependencies
  • pip install --quiet --upgrade tb-nightly or tensorboard, not both
• This project has been setup in Google Colaboratory Notebook. To have better experience and to keep focus on main thing, use Google Colaboratory Notebooks for easy environment setup.
• Check out https://www.tensorflow.org/federated/ for more details on tensorflow_federated

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Getting started

• Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)
• Clone your fork down to your local machine

git clone https://github.com/your-username/mage-Classification-by-Federated-Learning.git

• Create a branch

git checkout -b branch-name

• Make your changes
• Commit and push

git add .
git commit -m 'Commit message'
git push origin branch-name

• Create a new pull request from your forked repository (Click the New Pull Request button located at the top of your repo)
• Wait for your PR review and merge approval!
• Star this repository if you had fun!

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Contributors

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Goals

• Implementation of self-customized models on your own.
• Extend the application of Federated Learning to various problems related to data privacy concerns.
• Have a better understanding in the sub-field of implementing data models using Federated Learning approach.