 Here is a simplified example of how you might set up a federated learning pipeline using FATE library in Python. Note that this is a basic example and might need adjustments based on your specific use case.

```python
import fate_flow as fl
import fate_flow.contrib.data_source.mysql as mysql_source
import fate_flow.contrib.transforms as T
import fate_flow.contrib.estimators as estimators
import fate_flow.contrib.evaluation as evaluation

def create_pipeline(config):
    # Define the local data source
    local_data_source = mysql_source.MySQLDataSource(
        user=config['local_user'],
        password=config['local_password'],
        host=config['local_host'],
        db=config['local_db'],
        table=config['local_table']
    )

    # Define the remote data source
    remote_data_source = mysql_source.MySQLDataSource(
        user=config['remote_user'],
        password=config['remote_password'],
        host=config['remote_host'],
        db=config['remote_db'],
        table=config['remote_table']
    )

    # Define the pipeline
    pipeline = fl.Pipeline(pipeline_name='federated_learning_pipeline')

    # Add data reading and transformation stages
    pipeline = pipeline.add_stage(
        name='read_local_data',
        operator=fl.data_source_operator(local_data_source)
    )
    pipeline = pipeline.add_stage(
        name='transform_local_data',
        operator=T.TransformChain(
            T.SampleData(sample_rate=0.1),
            T.FeatureBinning(),
            T.OneHotEncoder(),
        )
    )
    pipeline = pipeline.add_stage(
        name='read_remote_data',
        operator=fl.data_source_operator(remote_data_source)
    )
    pipeline = pipeline.add_stage(
        name='transform_remote_data',
        operator=T.TransformChain(
            T.SampleData(sample_rate=0.1),
            T.FeatureBinning(),
            T.OneHotEncoder(),
        )
    )

    # Add local baseline model and secure boosting stages
    pipeline = pipeline.add_stage(
        name='local_baseline_model',
        operator=estimators.LogisticRegression()
    )
    pipeline = pipeline.add_stage(
        name='secure_boosting',
        operator=estimators.SecureBoosting(num_boost_round=10)
    )

    # Add evaluation stage
    pipeline = pipeline.add_stage(
        name='evaluation',
        operator=evaluation.Evaluator(metric_keys=['accuracy'])
    )

    # Compile and fit the pipeline
    pipeline.compile()
    pipeline.fit()

    # Print the evaluation summary
    print(pipeline.evaluator.get_metrics())

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', type=str, required=True)
    args = parser.parse_args()
    create_pipeline(args.config)
```

This code assumes that you have a configuration file with the necessary settings for the local and remote data sources, as well as the other configuration options. The configuration file should be in JSON format and might look something like this:

```json
{
    "local_user": "your_local_user",
    "local_password": "your_local_password",
    "local_host": "your_local_host",
    "local_db": "your_local_db",
    "local_table": "your_local_table",
    "remote_user": "your_remote_user",
    "remote_password": "your_remote_password",
    "remote_host": "your_remote_host",
    "remote_db": "your_remote_db",
    "remote_table": "your_remote_table"
}
```