# Federated Learning in Healthcare

This project demonstrates Federated Learning applied to healthcare data for predicting patient readmission risk. Federated Learning is a privacy-preserving machine learning technique that enables multiple parties to collaboratively train a model without sharing their raw data.

## Table of Contents

- [Introduction](#introduction)

- [Data](#data)

- [Setup](#setup)

- [Implementation](#implementation)

- [Results](#results)

- [License](#license)

## Introduction

In the healthcare domain, patient data privacy is of utmost importance. Federated Learning offers a solution where multiple hospitals can collaboratively build a predictive model without sharing sensitive patient data. Instead, they exchange model updates during training, ensuring privacy and compliance with data protection regulations.

This project uses synthetic data to illustrate the Federated Learning process. Real-world implementation would involve integrating with actual healthcare data while adhering to ethical considerations and data privacy policies.

## Data

For demonstration purposes, synthetic data is used to simulate electronic health records of diabetes patients. Each virtual hospital (two hospitals in this example) holds its data locally.

## Setup

To run the Federated Learning example, ensure you have the following libraries installed:

```bash

pip install syft numpy pandas torch torchvision

```

## Implementation

The implementation is done in Python using PySyft library, which extends PyTorch for Federated Learning. The process involves:

1. Generating synthetic data for two virtual hospitals.

2. Sharing data and labels securely with respective virtual hospitals using PySyft.

3. Defining a Federated Learning model (neural network).

4. Training the model collaboratively on each virtual hospital's data.

5. Aggregating model updates through simple averaging.

6. Obtaining the final model for prediction.

## Results

The Federated Learning model is trained on the synthetic healthcare data for predicting patient readmission risk. The model's performance is evaluated based on accuracy, precision, recall, F1-score, and other relevant metrics.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.