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This project utilizes federated learning with XGBoost for early cardiovascular disease detection, ensuring data privacy through Federated XGBoost Bagging. Built on the Flower architecture, it enables collaborative model training across decentralized healthcare datasets.

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mrinshad/CardioFed

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pip

Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.

pip install -r requirements.txt

Run Federated Learning with XGBoost and Flower

Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:

python3 server.py

Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminal windows and run the following commands.

Start client 1 on the first terminal:

python3 client.py --partition-id=0 --dataset=./Datasets/data1.csv

Start client 2 on the second terminal:

python3 client.py --partition-id=1 --dataset=./Datasets/data2.csv 

Start client 3 on the third terminal:

python3 client.py --partition-id=2 --dataset=./Datasets/data3.csv

Start client 4 on the fourth terminal:

python3 client.py --partition-id=3 --dataset=./Datasets/data4.csv

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This project utilizes federated learning with XGBoost for early cardiovascular disease detection, ensuring data privacy through Federated XGBoost Bagging. Built on the Flower architecture, it enables collaborative model training across decentralized healthcare datasets.

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