Federated learning, a decentralized model training paradigm, is implemented using the Flower framework in this research. The study focuses on predicting heart disease collaboratively across multiple clients while preserving data privacy. A custom neural network model is trained on clients, and the federated learning strategy of Flower facilitates model updates aggregation. The experiments showcase the effectiveness of collaborative model training, emphasizing data privacy preservation and reduced communication overhead. The results contribute to the growing field of federated learning applications and highlight the unique features of the Flower framework.

Prerequisites Before running this project, ensure you have the following dependencies installed:

Flower TensorFlow Pandas Keras

How to Run Server Setup: Open a terminal and run the following command to start the server:

python server.py 5000 Replace 5000 with your desired port number if necessary.

Client Setup: Open new terminals for each client you want to run. In each terminal, run the following command to start a client: python client.py 5000 Again, replace 5000 with the same port number used for the server if you changed it.

Accessing the Interface: Once the server and clients are connected successfully, open a web browser and navigate to http://localhost:8000. You will see the user interface.

Running Federated Learning (FL):

On the interface, there will be a button to start FL. Click on it to initiate the federated learning process. After the FL completion, you can click on the "Check Results" button. Form Submission:

Fill out the form with the required details. Submit the form to get the results.