Chest X-ray Federated Learning

The Chest X-ray Federated Learning project is a Python-based implementation of federated learning techniques applied to chest X-ray images. It aims to enable collaborative learning across multiple healthcare institutions while preserving data privacy. This README file provides an overview of the project, its features, installation instructions, and other important details.

Features

Federated Learning: The project implements federated learning techniques, allowing multiple healthcare institutions to collaboratively train machine learning models without sharing raw data.
Chest X-ray Dataset: The project utilizes a chest X-ray dataset, which includes a collection of chest X-ray images and their corresponding labels.
Client-Server Architecture: The federated learning system follows a client-server architecture, where a central server coordinates the training process, and clients (healthcare institutions) contribute to model training using their local data.
Privacy Preservation: The project ensures data privacy by using techniques like federated averaging, secure aggregation, or differential privacy to protect sensitive patient information during the federated learning process.
Model Evaluation: The implementation includes evaluation mechanisms to assess the performance of the trained models using validation datasets.
Model Aggregation: The central server aggregates model updates from clients to create a global model, leveraging techniques like federated averaging to combine the local models effectively.

Installation

To set up the Chest Xray Federated Learning project, follow these steps:

Clone the project repository:

git clone https://github.com/MayankBharati/chest-xray-federated-learning.git

Install the required dependencies:
```
pip install -r requirements.txt
```
Configure the project settings. Update the configuration files to specify the server address, communication protocols, dataset paths, and other relevant parameters.
Prepare the dataset. Ensure that the chest X-ray dataset is available and properly formatted. Modify the data loading scripts if necessary to match the dataset structure.
The federated learning process will commence, with the clients training the models collaboratively under the coordination of the server.

Usage

The Chest Xray Federated Learning project consists of two primary components: the server and the client.

The server acts as the central coordinator and manages the training process. It communicates with the clients, aggregates model updates, and performs global model updates.
The client(s) participate in the federated learning process by training models locally using their data. They communicate with the server to exchange model updates and receive global model updates.

Detailed usage instructions and code examples can be found in the project's documentation.

Contributing

Contributions to the Chest Xray Federated Learning project are welcome! If you would like to contribute new features, enhancements, or bug fixes, please submit a pull request to the project repository. Follow the established code style and guidelines when making contributions.

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