Federated Neural Network Defense Against Adversarial Attacks
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Solution Overview
Problem
Neural networks in federated training systems are vulnerable to adversarial attacks, which can compromise privacy and data security by allowing attackers to steal data and misclassify samples, posing a threat to the integrity of the training process.
Innovation Solution
Implement a defense mechanism that involves simulating potential attacks internally within the training devices, using Generative Adversarial Networks (GANs) to generate fake data similar to the original data, and comparing the performance of the neural network before and after simulated adversarial training to detect and mitigate harmful updates by modifying parameters such as gradients and weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated training is implemented to distribute neural network training across multiple devices, then training scalability and data privacy are improved, but the system becomes vulnerable to adversarial attacks and poisoning threats
Solution Approach 1:
The patent applies preliminary anti-action by implementing a defense mechanism that proactively identifies and mitigates adversarial threats before they can compromise the federated training system. The server monitors client updates for signs of poisoning attacks and applies corrections to neutralize harmful effects, preventing attacks from succeeding rather than reacting after damage occurs.
Solution Approach 2:
The patent converts harmful adversarial updates into beneficial information by using them as training signals for the defense mechanism. The server detects poisoned updates, analyzes their characteristics, and uses this information to improve detection accuracy and protect the federated training system, turning attacks into opportunities for strengthening security.
2Productivity
If gradient updates are shared among clients in federated learning, then collaborative model training is improved, but privacy of training data and model parameters is compromised
Solution Approach 1:
The patent introduces the server as an intermediary that mediates between clients during federated training. The server receives gradient updates from clients, applies defense mechanisms to remove malicious content, and aggregates updates to create the global model. This intermediary role enables collaborative training while protecting privacy by filtering sensitive information before it is fully distributed.
3Reliability
If defense mechanisms are added to detect adversarial attacks, then system security is improved, but computational overhead and training complexity increase
Solution Approach 1:
The patent implements feedback by creating a closed-loop defense system where the server continuously monitors client updates, detects adversarial patterns, applies corrections, and uses the results to improve future detection. The defense mechanism learns from detected attacks and adjusts its parameters, providing feedback that enhances security without requiring complete system redesign.
Data Source
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Figure 4a
AI summary
There is provided an apparatus comprising means for receiving, by an apparatus comprising a neural network, parameters, wherein the neural network comprises a classifier and the apparatus is responsible for training for at least one class of a plurality of classes; updating the parameters of the neural network with the received parameters to obtain a current neural network; training the current neural network for the at least one class; computing first updated parameters based on the training of the current neural network; updating the parameters of the current neural network with the first updated parameters to obtain a first updated neural network; training the first updated neural network for other classes of the plurality of classes than the at least one class using data similar to data belonging to the at least one class; computing second updated parameters based on the training of the first updated neural network; updating the first updated parameters with the second updated parameters to obtain a second updated neural network; and comparing performance of the current neural network and the second updated neural network.