 ```python
import sys
from fate_flow.settings import BASE_DIR
from fate_flow.thirdparty.fate_arch.computing import FateBoardCast, FateFMG
from fate_flow.thirdparty.fate_arch.session import FateSession
from fate_flow.entity.data_job import DataJob
from fate_flow.entity.model_job import ModelJob
from fate_flow.entity.table import Table
from fate_flow.thirdparty.fate_arch.utils.serializable import Serializable
from fate_flow.component.base import DslComponent
from fate_flow.component.data_preprocess.transformation.transformation import (
    Transformation,
)
from fate_flow.component.data_preprocess.feature_binning.feature_binning import (
    FeatureBinning,
)
from fate_flow.component.data_preprocess.one_hot_encoding.one_hot_encoding import (
    OneHotEncoding,
)
from fate_flow.component.data_preprocess.sampling.sampling import Sampling
from fate_flow.component.evaluation.metric.metric import Metric
from fate_flow.component.evaluation.summary.summary import Summary
from fate_flow.component. federation_learn.logistic_regression.logistic_regression import LogisticRegression
from fate_flow.component. federation_learn.local_baseline.local_baseline import LocalBaseline
from fate_flow.component. federation_learn.secure_boosting.secure_boosting import SecureBoosting

class FederatedLearningPipeline(DslComponent):
    def __init__(self, config_file):
        super(FederatedLearningPipeline, self).__init__()
        self.config_file = config_file

    def initialize(self, context):
        self.session = FateSession(context.get_component_property("session_id"))
        self.data_job_conf = Serializable.loads(context.get_component_property("data_job_conf"))
        self.model_job_conf = Serializable.loads(context.get_component_property("model_job_conf"))

    def execute(self, data_inputs, context):
        # Create data job
        data_job = DataJob()
        data_job.description = "Federated Learning Data Job"
        data_job. tables = [
            Table(
                name=self.data_job_conf["tables"]["train"]["name"],
                description="Training data table",
                data_type="HDFS",
                partition_file="",
                property="",
                data_source={
                    "files": [
                        f"{BASE_DIR}/data/train_{party}.csv"
                        for party in ["guest", "host"]
                    ]
                },
            )
        ]

        # Submit data job
        data_job_id = self.session.submit_data_job(data_job).job_id

        # Wait for data job to complete
        self.session.wait(data_job_id)

        # Create components
        transformation = Transformation(
            component_properties=self.model_job_conf["components"]["transformation"]
        )
        sampling = Sampling(
            component_properties=self.model_job_conf["components"]["sampling"]
        )
        feature_binning = FeatureBinning(
            component_properties=self.model_job_conf["components"]["feature_binning"]
        )
        one_hot_encoding = OneHotEncoding(
            component_properties=self.model_job_conf["components"]["one_hot_encoding"]
        )
        logistic_regression = LogisticRegression(
            component_properties=self.model_job_conf["components"]["logistic_regression"]
        )
        local_baseline = LocalBaseline(
            component_properties=self.model_job_conf["components"]["local_baseline"]
        )
        secure_boosting = SecureBoosting(
            component_properties=self.model_job_conf["components"]["secure_boosting"]
        )
        metric = Metric(
            component_properties=self.model_job_conf["components"]["metric"]
        )
        summary = Summary(
            component_properties=self.model_job_conf["components"]["summary"]
        )

        # Set up pipeline
        pipeline = FateFMG()
        pipeline.add_component(transformation)
        pipeline.add_component(sampling)
        pipeline.add_component(feature_binning)
        pipeline.add_component(one_hot_encoding)
        pipeline.add_component(logistic_regression)
        pipeline.add_component(local_baseline)
        pipeline.add_component(secure_boosting)
        pipeline.add_component(metric)
        pipeline.add_component(summary)

        # Set up communication
        broadcast = FateBoardCast()
        broadcast.add_role("guest")
        broadcast.add_role("host")

        # Compile and fit pipeline
        self.session.compile_and_fit(
            data_job_id,
            pipeline,
            broadcast,
            ModelJob(
                model_name="federated_learning",
                model_version="1.0.0",
                description="Federated Learning Model Job",
                model_table="model_table",
                model_file="model_file",
                tables=[
                    Table(
                        name="model_table",
                        description="Model table",
                        data_type="FATE_FLOW",
                        partition_file="",
                        property="",
                        model_data_source={},
                    ),
                    Table(
                        name="model_file",
                        description="Model file",
                        data_type="FATE_FLOW",
                        partition_file="",
                        property="",
                        model_data_source={},
                    ),
                ],
            ),
        )

        # Print evaluation summary
        print(self.session.get_component_instance(summary.component_id).get_summary())

if __name__ == "__main__":
    FederatedLearningPipeline(sys.argv[1]).deploy()
```