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+# 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.