df_hyperparams.py

hyperparams = {
    'n_runners': 10,
    'sample_size': 5,
    'rounds': 100,
    'combine': 'weighted', # or 'mean'
    'partition_params': {
        'scheme': 'uniform' # stratified, non-iidness, noise injection etc.
    },
    'runner_hyperparams': {
        'epochs': 1,
        'lr': 0.15,
        'batch_size': 0
    }
}

fed_avg = FedAvg(**hyperparams)
fed_avg.fit(X_train, y_train)
preds = fed_avg.predict(X_test)

print(classification_report(y_test, preds, target_names=['class_0', 'class_1']))
print(fed_avg.intercept_)
print(fed_avg.coef_)