Credit Card Fraud Detection Using Federated Learning And Split Learning

Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch

AI & Machine LearningJupyter NotebookGPL-3.0

Abstract

Credit Card Fraud Detection Using Federated Learning And Split Learning is an open-source AI & Machine Learning project. Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch. In this project, we show a comparison between Federated Learning and Split Learning for credit card fraud detection dataset. It is built using Jupyter Notebook, PyTorch. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

In this project, we show a comparison between Federated Learning and Split Learning for credit card fraud detection dataset.

This dataset has been processed, split into train and test, and it was used for training, testing, and comparison. Smote1.ipynb is to generate somte file for the dataset.

2. Objective

Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch

This project demonstrates how Jupyter Notebook, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Jupyter NotebookPyTorch

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/PR-Desai2226/Credit-card-fraud-detection-using-Federated-Learning-and-Split-Learning.git
cd Credit-card-fraud-detection-using-Federated-Learning-and-Split-Learning

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by PR-Desai2226 and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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