```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=1,
            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:
            # Handle abort signals
            # Update local model weights with received weights
            # Load Ditto personalized model
            # Perform local training on reference model and personalized model
            # Validate Ditto model each round
            # Compute delta model
            # Return shareable object with updated local model
            pass  # Placeholder for the actual implementation
        except KeyboardInterrupt:
            print("Training aborted.")
```