Documentation for Gllneuroninversion Module

GLLNeuronInversionAttack

Bases: ModelAttack

Implements a neuron inversion attack on the received model weights.

This attack aims to invert the values of neurons in specific layers by replacing their values with random noise, potentially disrupting the model's functionality during aggregation.

Parameters:

Name	Type	Description	Default
engine	object	The training engine object that manages the aggregator.	required
_	any	A placeholder argument (not used in this class).	required

Source code in nebula/addons/attacks/model/gllneuroninversion.py

class GLLNeuronInversionAttack(ModelAttack):
    """
    Implements a neuron inversion attack on the received model weights.

    This attack aims to invert the values of neurons in specific layers 
    by replacing their values with random noise, potentially disrupting the model's 
    functionality during aggregation.

    Args:
        engine (object): The training engine object that manages the aggregator.
        _ (any): A placeholder argument (not used in this class).
    """
    def __init__(self, engine, attack_params):
        """
        Initializes the GLLNeuronInversionAttack with the specified engine.

        Args:
            engine (object): The training engine object.
            _ (any): A placeholder argument (not used in this class).
        """
        super().__init__(engine)
        self.round_start_attack = int(attack_params["round_start_attack"])
        self.round_stop_attack = int(attack_params["round_stop_attack"])

    def model_attack(self, received_weights):
        """
        Performs the neuron inversion attack by modifying the weights of a specific 
        layer with random noise.

        This attack replaces the weights of a chosen layer with random values, 
        which may disrupt the functionality of the model.

        Args:
            received_weights (dict): The aggregated model weights to be modified.

        Returns:
            dict: The modified model weights after applying the neuron inversion attack.
        """
        logging.info("[GLLNeuronInversionAttack] Performing neuron inversion attack")
        lkeys = list(received_weights.keys())
        logging.info(f"Layer inverted: {lkeys[-2]}")
        received_weights[lkeys[-2]].data = torch.rand(received_weights[lkeys[-2]].shape) * 10000
        return received_weights

__init__(engine, attack_params)

Initializes the GLLNeuronInversionAttack with the specified engine.

Parameters:

Name	Type	Description	Default
engine	object	The training engine object.	required
_	any	A placeholder argument (not used in this class).	required

Source code in nebula/addons/attacks/model/gllneuroninversion.py

def __init__(self, engine, attack_params):
    """
    Initializes the GLLNeuronInversionAttack with the specified engine.

    Args:
        engine (object): The training engine object.
        _ (any): A placeholder argument (not used in this class).
    """
    super().__init__(engine)
    self.round_start_attack = int(attack_params["round_start_attack"])
    self.round_stop_attack = int(attack_params["round_stop_attack"])

model_attack(received_weights)

Performs the neuron inversion attack by modifying the weights of a specific layer with random noise.

This attack replaces the weights of a chosen layer with random values, which may disrupt the functionality of the model.

Parameters:

Name	Type	Description	Default
received_weights	dict	The aggregated model weights to be modified.	required

Returns:

Name	Type	Description
dict		The modified model weights after applying the neuron inversion attack.

Source code in nebula/addons/attacks/model/gllneuroninversion.py

def model_attack(self, received_weights):
    """
    Performs the neuron inversion attack by modifying the weights of a specific 
    layer with random noise.

    This attack replaces the weights of a chosen layer with random values, 
    which may disrupt the functionality of the model.

    Args:
        received_weights (dict): The aggregated model weights to be modified.

    Returns:
        dict: The modified model weights after applying the neuron inversion attack.
    """
    logging.info("[GLLNeuronInversionAttack] Performing neuron inversion attack")
    lkeys = list(received_weights.keys())
    logging.info(f"Layer inverted: {lkeys[-2]}")
    received_weights[lkeys[-2]].data = torch.rand(received_weights[lkeys[-2]].shape) * 10000
    return received_weights