 Here is a simplified example of how you might set up a federated learning pipeline using the FATE library. Please note that this is a basic example and might need to be adjusted based on your specific use case.

```python
import fate_flow as fl
import fate_flow.contrib.algo.lr as lr
import fate_flow.contrib.algo.sbt as sbt
import fate_flow.contrib.transforms as transforms
import fate_flow.contrib.data_gen as data_gen
import fate_flow.contrib.data_gen.data_gen_util as data_gen_util
import fate_flow.contrib.data_gen.data_gen_util_v2 as data_gen_util_v2
import fate_flow.contrib.data_gen.data_gen_util_v3 as data_gen_util_v3

def create_pipeline(config):
    # Initialize data and variable
    init_data_and_variable = fl.node(fl.server_side_func(data_gen_util_v3.init_data_and_variable))

    # Data and header alignment
    data_and_header_alignment = fl.node(fl.server_side_func(data_gen_util_v3.data_and_header_alignment))

    # Quantile binning and count
    quantile_binning_and_count = fl.node(fl.server_side_func(data_gen_util_v3.quantile_binning_and_count))

    # One-hot encoding
    one_hot = fl.node(fl.server_side_func(data_gen_util_v3.one_hot))

    # Cross entropy for one-hot
    cross_entropy_for_one_hot = fl.node(fl.server_side_func(data_gen_util_v3.cross_entropy_for_one_hot))

    # Logistic regression
    lr_pipeline = lr.lr_train_pipeline()

    # Secure boosting
    sbt_pipeline = sbt.sbt_train_pipeline()

    # Create guest and host components
    guest_components = [
        init_data_and_variable,
        data_and_header_alignment,
        quantile_binning_and_count,
        one_hot,
        cross_entropy_for_one_hot,
        lr_pipeline,
    ]

    host_components = [
        init_data_and_variable,
        data_and_header_alignment,
        quantile_binning_and_count,
        one_hot,
        cross_entropy_for_one_hot,
        sbt_pipeline,
    ]

    # Create pipeline config
    pipeline_config = fl.PipelineConfig(
        guest_components=guest_components,
        host_components=host_components,
        local_baseline_model=lr_pipeline,
    )

    # Compile and fit the pipeline
    pipeline = fl.compile(pipeline_config)
    pipeline.fit()

    # Print the summary of the evaluation components
    print(pipeline.evaluate())

    return pipeline

def main(config_file):
    # Load the configuration
    config = fl.parse_config_file(config_file)

    # Create the pipeline
    pipeline = create_pipeline(config)

    # Run the pipeline
    pipeline.run()
```

This code defines a function `create_pipeline` that creates a federated learning pipeline with the specified components. The `main` function loads a configuration file, creates the pipeline, and runs it. You would need to provide your own implementation for the data source and table reading based on your specific use case.