Federated Machine Learning Examples

This repository contains a collection of Jupyter Notebook examples demonstrating the implementation of federated machine learning on text and image data. The examples showcase different approaches such as Federated Averaging, Federated MinLoss, and Federated Top-Alpha.

Notebooks

• 19PD05_FEDML.ipynb: Federated ML implementation using Colaboratory
• FEDAVG_DIASATER_TWEETS.ipynb: Federated ML implementation on twitter comment hatred, using fedAVG method.
• FEDMinLoss_DISASTER_TWEETS.ipynb: Federated ML implementation on twitter comment hatred, using fedminlossmethod.
• FEDTopALPH_DISASTER_TWEETS.ipynb: Federated ML implementation on twitter comment hatred, using fedtopalpha method.
• FedAVG_MNIST.ipynb: Federated ML implementation on text and image data, created last month.
• FedMINLoss_MNIST.ipynb: Federated ML implementation on text and image data, created last month.
• FedTopALPHA_MNIST.ipynb: Federated ML implementation on text and image data, created last month.
• Inference.txt: Results and inferences from the federated ML models.

Feel free to explore the notebooks to understand and experiment with federated machine learning techniques on different datasets.

Preview

Usage

1. Clone the repository: git clone https://github.com/Lagstill/PyNLPFederated.git
2. Navigate to the cloned repository
3. Open and run the desired Jupyter Notebook example using a Python environment or an online notebook platform like Colaboratory.

Please refer to the individual notebook files for more details about the specific implementations.

References

1. (Pysyft)[https://github.com/OpenMined/PySyft/tree/master/packages]
2. (Paper Implemented above is accessed here)[https://arxiv.org/pdf/2109.02351.pdf]