Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

Abstract

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Abstract

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.
Paper Structure (8 sections, 3 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 8 sections, 3 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Proposed Quantum Federated Autoencoder
  4. Experimental Setup
  5. Training Configuration
  6. Thresholding for Anomaly Detection
  7. Evaluation: Results and Discussion
  8. Conclusion

Figures (4)

  • Figure 1: Quantum Federated Autoencoder Framework for Anomaly Detection.
  • Figure 2: Quantum Autoencoder with 4 qubits: 3 latent, 1 trash.
  • Figure 3: Network topology with Coordinator (C), Routers (R), Edge nodes (E) and Attack node (A).
  • Figure 4: Comparison of FL methods for 3 devices over 5 rounds: (a) Hierarchical FL and (b) Standard FedAvg FL.