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Updated: May 2, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Federated quantum-inspired anomaly detection using collaborative neural clients.

Deepthi Godavarthi1, Venkata Charan Sathvik Rekapalli1, Sribidhya Mohanty2

  • 1School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, India.

Frontiers in Artificial Intelligence
|September 11, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel quantum-inspired federated learning approach for anomaly detection, ensuring data privacy and scalability in distributed environments. The method achieves high accuracy while safeguarding sensitive information, paving the way for secure AI applications.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Quantum Computing

Background:

  • Current deep-learning and federated methods for anomaly detection face privacy risks due to data aggregation.
  • Existing systems struggle with scalability and adaptability in heterogeneous, distributed environments.
  • Quantum-inspired paradigms offer potential improvements in speed and security but are underexplored in anomaly detection.

Purpose of the Study:

  • To propose a novel quantum-inspired federated learning approach for anomaly detection.
  • To address privacy concerns by avoiding central data aggregation.
  • To enhance scalability and adaptability for distributed systems and explore quantum computing integration.

Main Methods:

  • A client-server architecture is employed, where clients train local feedforward neural networks on private data subsets.
Keywords:
TCP based model communicationanomaly detectiondistributed systemsfederated learningprivacy-preserving AIquantum-inspired neural networks

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Last Updated: May 2, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Published on: September 8, 2023

1.1K
  • Clients transmit only model parameters, not raw data, to the server for aggregation using the FedAvg algorithm.
  • The system is designed for classical deep learning with future flexibility for quantum machine learning integration.

    • Main Results:

    • The framework achieved up to 79% accuracy in anomalous detection.
    • Effective learning was demonstrated across distributed clients without data leakage.
    • The system maintained high performance while ensuring complete data privacy.

    Conclusions:

    • The proposed approach enables privacy-preserving anomaly detection in diverse domains, offering a scalable framework.
    • Quantum-inspired compatibility ensures future-proofing, enhanced security, and faster processing.
    • The system is suitable for critical sectors like cybersecurity, finance, and healthcare due to its secure, distributed nature.