```python
from monai.networks.nets import UNet
from torch.optim import Adam

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, training_config_filename, num_aggregation_epochs, num_ditto_model_epochs, training_task_name):
        self.training_config_filename = training_config_filename
        self.num_aggregation_epochs = num_aggregation_epochs
        self.num_ditto_model_epochs = num_ditto_model_epochs
        self.training_task_name = training_task_name
        self.ditto_helper = SupervisedPTDittoHelper()

    def train_config(self):
        super().__init__(self.training_config_filename)
        self.model = UNet(
            dimensions=3,
            in_channels=1,
            out_channels=2,
            channels=(16, 32, 64, 128, 256),
            strides=(2, 2, 2, 2),
            num_res_units=2,
        )
        self.optimizer = Adam(self.model.parameters(), lr=1e-4)
        self.ditto_helper.setup(self.model, self.optimizer)

    def train(self):
        try:
            for epoch in range(self.num_aggregation_epochs):
                # Handle abort signals
                # Update local model weights with received weights
                _receive_and_update_model()
                # Load Ditto personalized model
                load_local_model(self.model, "ditto_personalized_model.pth")
                for ditto_epoch in range(self.num_ditto_model_epochs):
                    # Perform local training on reference model and personalized model
                    self.ditto_helper.train_epoch()
                # Validate the Ditto model each round
                self.ditto_helper.validate()
                # Compute the delta model
                delta_model = compute_model_diff(self.model, "reference_model.pth", "ditto_personalized_model.pth")
                # Return a shareable object with the updated local model
                submit_model(delta_model)
        except KeyboardInterrupt:
            # Handle abort operation
            pass
```