XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers

1. 1
   Set Up a Baseline Federated Learning Environment

   Initialize a central server and multiple clients using an FL framework like Flower or TensorFlow Federated. Ensure your setup includes a global model and a dataset split across clients.
2. 2
   Define the Malicious Objective

   Determine the specific goal of the attack. This could be causing misclassification for a target class, backdoor insertion, or degrading overall model performance on specific inputs.
3. 3
   Implement a Malicious Client Strategy

   Design a client that generates poisoned local model updates without requiring coordination with other attackers. This typically involves manipulating local training data (e.g., label flipping, data poisoning) or directly altering gradients before sending them to the server.
4. 4
   Integrate Byzantine-Robust Aggregation

   Configure your FL server to use a Byzantine-robust aggregation mechanism (e.g., Krum, Trimmed Mean, Median) to simulate the defense XFED aims to bypass. This is crucial for evaluating the attack's effectiveness.
5. 5
   Execute Federated Learning Rounds with Attackers

   Run the FL training process for several rounds, allowing the malicious client(s) to submit their crafted poisoned updates alongside benign client updates. Observe the server's aggregation process.
6. 6
   Evaluate Attack Effectiveness

   After training, evaluate the global model's performance. Measure accuracy on benign test data and, critically, on targeted (attacked) data or specific inputs designed to reveal the attack's success, demonstrating the bypass of Byzantine defenses.

import flwr as fl
import numpy as np

# Define a simple client for demonstration
class FlowerClient(fl.client.NumPyClient):
    def get_parameters(self, config):
        # In a real scenario, this would return model weights
        return [np.array([0.1, 0.2, 0.3])]

    def fit(self, parameters, config):
        # Simulate training: update parameters (e.g., add noise for poisoning later)
        new_params = [p + np.random.rand(*p.shape) * 0.01 for p in parameters]
        num_examples = 10 # Simulate data size
        return new_params, num_examples, {}

    def evaluate(self, parameters, config):
        # Simulate evaluation
        loss = 0.5
        accuracy = 0.9
        num_examples = 5 # Simulate data size
        return loss, num_examples, {"accuracy": accuracy}

# Start a simple Flower server with a basic strategy
# For a real XFED simulation, you'd use a Byzantine-robust strategy here
if __name__ == "__main__":
    strategy = fl.server.strategy.FedAvg(min_available_clients=2, fraction_fit=1.0, fraction_evaluate=1.0)
    fl.server.start_server(server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3), strategy=strategy)