FLCore

Library of Federated Learning models integrated within the DataTools4Heart project.

Currently implemented models

Model	Aggregation method	Alias	Link
Logistic regression	FedAvg	logistic_regression	flower.dev/docs/framework/quickstart-scikitlearn.html
SGD Classifier	FedAvg	lsvc	flower.dev/docs/framework/quickstart-scikitlearn.html
Elastic Net	FedAvg	elastic_net	flower.dev/docs/framework/quickstart-scikitlearn.html
Random Forest	Custom	random_forest	Random Forest Based on Federated Learning for Intrusion Detection
Weighted Random Forest	Custom	weighted_random_forest	Random Forest Based on Federated Learning for Intrusion Detection
XGBoost	FedXgbNnAvg	xgb	Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
Deep Learning	FedAvg	nn	https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html

Quickstart

Install necessary dependencies:

pip install -r requirements.txt

To start a federated training run:

python run.py

it will automatically start a server and client processes defined in config.yaml

Step by step

Also, you can do it manually by running:

python server.py

And then, preferably in a separate shell window for clean output, start clients with their corresponding ids:

python client.py 1

python client.py 2

Configuration file

The federated training parameters are defined in config.yaml The most important parameters are:

• num_clients (number of clients needed in a federated training)
• num_rounds (number of training rounds)
• model (machine learning model with it's federated implementation)

Data loader

To train on your own dataset add a loading method in the datasets.py file and a corresponding entry in the load_dataset() method.

Loading method

XY = Tuple[np.ndarray, np.ndarray]
Dataset = Tuple[XY, XY]

def load_my_dataset(data_path, center_id=None) -> Dataset:

Note

It is important to note that each client can only use it's subset of data corresponding to it's institution. When deployed in a real federated setting, each client will access the available data through the provided data_path in config.yaml file. To enable this behaviour in simulated setting, a dataset loading method should accept center_id argument in order to load only a specific part of a dataset and simulate distributed data scheme.

Contributing

To add a new model to the framework two methods need to be implemented:

For server side:

def get_server_and_strategy(config, data = None) -> Tuple[Optional[flwr.server.Server], flwr.server.strategy.Strategy]:

which returns Flower Server object (optional) and Flower Strategy object.

For client side:

def get_client(config, data) -> flwr.client.Client:

This method should return the initialized client with data loaded specifically for this data center.

Contribution steps

After implementing the necessary methods follow the remaining steps:

1. Create a new branch in flcore repository
2. Copy your model package to flcore/models directory
3. Add cases for the new model in server_selector.py and client_selector.py modules in flcore/ directory
4. Add the model to the available models table in README.md file
5. Open a Pull Request and wait for review