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Related Experiment Video

Updated: May 5, 2026

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

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

Published on: September 8, 2023

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Trust-Aware and Energy-Efficient Federated Learning for Secure Sensor Networks at the Edge.

Manuel J C S Reis1

  • 1Engineering Department and IEETA, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces a trust-aware federated learning framework for secure sensor networks. It enhances model accuracy and reduces energy consumption by managing node trust and communication efficiency.

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Area of Science:

  • Edge computing
  • Machine learning
  • Sensor networks

Background:

  • Growing need for secure, trustworthy, and energy-efficient learning in edge sensor networks.
  • Federated learning offers privacy preservation but often lacks robust trust management and efficiency.
  • Resource constraints in edge environments necessitate specialized learning frameworks.

Purpose of the Study:

  • To propose a trust-aware and energy-efficient federated learning framework for secure sensor networks.
  • To address limitations in existing federated approaches regarding trust, communication, and energy.
  • To enhance the robustness and stability of collaborative learning in heterogeneous edge environments.

Main Methods:

  • Integration of lightweight trust metrics for node evaluation.
Keywords:
Internet of Thingsedge intelligenceenergy efficiencyfederated learningsecure distributed learningsensor networkstrust management

Related Experiment Videos

Last Updated: May 5, 2026

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

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

Published on: September 8, 2023

1.3K
  • Implementation of trust-driven model aggregation to weight client contributions.
  • Adaptive communication scheduling to optimize energy expenditure.
  • Dynamic weighting of client contributions based on trust and efficiency.

    • Main Results:

    • Improved model accuracy by up to 3.2% under heterogeneous conditions.
    • Reduced communication overhead by 28%.
    • Decreased cumulative energy consumption by 31%.
    • Enhanced robustness against adversarial participation.

    Conclusions:

    • The proposed framework effectively enhances security, trust, and energy efficiency in federated learning for sensor networks.
    • It offers a practical solution for resource-constrained edge environments.
    • The approach improves learning stability and accuracy while mitigating risks from unreliable nodes.