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Updated: Sep 18, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

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Federated Learning for a Dynamic Edge: A Modular and Resilient Approach.

Leonardo Almeida1,2, Rafael Teixeira1,2, Gabriele Baldoni3

  • 1Intituto de Telecomunicações, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

This study introduces a resilient Federated Learning (FL) framework for edge computing. The modular design enhances fault tolerance and communication efficiency, with Zenoh proving most effective.

Keywords:
distributed machine learningfault tolerantfederated learningresilient

Related Experiment Videos

Last Updated: Sep 18, 2025

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

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Published on: September 8, 2023

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Growing demand for distributed machine learning (ML) in edge environments.
  • Challenges in fault tolerance, elasticity, and communication efficiency for Federated Learning (FL).
  • Need for adaptable frameworks in dynamic, resource-constrained settings.

Purpose of the Study:

  • Propose a novel modular and resilient FL framework.
  • Address challenges of fault tolerance and communication efficiency in edge ML.
  • Enhance the adaptability of FL systems for diverse environments.

Main Methods:

  • Developed a modular FL framework with decoupled core functionalities.
  • Integrated various communication protocols (Zenoh, MQTT, Kafka) and FL paradigms.
  • Simulated probabilistic worker failures to test resilience and performance.

Main Results:

  • Framework demonstrated flexibility in integrating diverse communication protocols and FL approaches.
  • Protocol selection significantly impacts performance, especially in high-volume communication.
  • Zenoh showed the lowest overhead and highest efficiency among tested protocols.
  • Model training achieved convergence with a Matthews Correlation Coefficient (MCC) of 0.9453 despite simulated failures.

Conclusions:

  • The proposed modular framework enhances resilience and efficiency in Federated Learning.
  • Optimized communication protocol selection is crucial for performance in edge environments.
  • The framework effectively maintains FL operations under disruptive conditions, ensuring model convergence.