Overview

This page provides an overview of the Federated Learning module in MEDomicsLab, offering insights into both the application's interface and the backend package employed for conducting experiments.

Introduction

The Federated Learning Module in MEDomicsLab simulates the process of federated learning and allows for training models in a decentralized manner using multiple datasets. This approach preserves privacy and enhances data security by ensuring that data never leaves its original location.

Key Aspects of the Federated Learning Module:

Decentralized Training: Models are trained across multiple nodes without transferring raw data.
Privacy Preservation: Utilizing techniques like differential privacy to ensure data confidentiality.
Hyperparameter Optimization: Tools to automatically tune and optimize model hyperparameters for improved performance.
Transfer learning: Allowing the user to use pre-trained models to initialize the central server to improve the model performance

Video Tutorial

MEDfl package

The Federated Learning module in the MEDomicsLab application uses MEDfl in the backend, a standalone Python package designed for simulating federated learning.

You can also use MEDfl independently from the app to create your networks and pipelines directly with code. Below is a brief example demonstrating how to do that.

# install MEDfl 
pip install MEDfl

# import MEDfl
import MEDfl 

# ... import the rest of the dependencies here 

# Create a network "Net_1"
Net_1 = Network(name="Auto_Net")
Net_1.create_network()

# Create a MasterDataSet from Net_1
Net_1.create_master_dataset()

# FLsetup creation
autoFl  = FLsetup(name = "Flsetup_1", description = "The first fl setup",network = Net_1)
autoFl.create()

# Create node 
hospital = Node(name="hospital_1", train=1)
Net_1.add_node(hospital)
hospital.upload_dataset("hospital_1"+'_dataset', base_url + '/notebooks/data/nodesData/output_1'+'.csv')

# Create FLDataSet
fl_dataset = autoFl.create_federated_dataset(
                output="deceased",
                fit_encode=[],
                to_drop=["deceased"]
 )


# Load the pre-trained model
model = Model.load_model("../../notebooks/.ipynb_checkpoints/trainedModels/grid_search_classifier.pth")


# Create the strategy
aggreg_algo = Strategy(config['aggreg_algo'],
                                   fraction_fit=1.0,
                                   fraction_evaluate=1.0,
                                   min_fit_clients=2,
                                   min_evaluate_clients=2,
                                   min_available_clients=2,
                                   initial_parameters=global_model.get_parameters())
aggreg_algo.create_strategy()

# Create The server
server = FlowerServer(global_model,
                                  strategy=aggreg_algo,
                                  num_rounds=server_rounds,
                                  num_clients=len(fl_dataset.trainloaders),
                                  fed_dataset=fl_dataset,
                                  diff_privacy=config['dp_activate'],
                                  # You can change the resources located for each client based on your machine
                                  client_resources={
                                      'num_cpus': 1.0, 'num_gpus': 0.0}
                                  )
# Create the pipeline
ppl_1 = FLpipeline(name="the second fl_pipeline",
                               description="This is our first FL pipeline",
                               server=server)
            
# Run the Training of the model
history = ppl_1.server.run()

# Test the model 
report = ppl_1.auto_test()

For more detailed examples, you can check the tutorials on the GitHub repository.

Application Interface

The interface of the MEDfl module in the MEDomicsLab application provides a user-friendly space where you can visually manage and connect multiple nodes to create your federated learning pipelines. Each node type in the interface has a specific role and attributes, allowing you to build and customize your federated learning networks seamlessly.

Below is a table explaining the role and attributes of each node:

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