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Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments.

Ali Bemani1, Niclas Björsell1

  • 1Department of Electrical Engineering, Mathematics and Science, University of Gävle, 801 76 Gävle, Sweden.

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Summary

Industry 4.0 enables edge learning for predictive maintenance, but communication latency is a challenge. This study introduces a novel method for robust analog aggregation over-the-air in federated learning, enhancing efficiency and accuracy even with noisy channels.

Keywords:
analog aggregationchannel noiselow latencyover-the-air federated learningpredictive maintenance

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Area of Science:

  • Industrial IoT and Edge Computing
  • Artificial Intelligence and Machine Learning
  • Wireless Communications

Background:

  • Industry 4.0 drives the development of interconnected industrial assets, generating vast data for edge device learning.
  • Edge learning is crucial for applications like predictive maintenance (PM), but communication latency poses a significant bottleneck.
  • Federated Learning (FL) offers a privacy-preserving framework for distributed model training on edge devices.

Purpose of the Study:

  • To address communication latency in edge learning for Industry 4.0 applications.
  • To propose and evaluate an analog aggregation over-the-air approach for federated learning (FL) over wireless channels.
  • To mitigate performance degradation caused by channel noise in FL over-the-air communication and computation (FLOACC).

Main Methods:

  • Implemented analog aggregation over-the-air, leveraging waveform superposition for reduced communication latency.
  • Integrated a novel tracking-based stochastic approximation scheme with federated stochastic variance reduced gradient (FSVRG).
  • Developed a method to mitigate channel noise impact in FLOACC without increasing transmission power.

Main Results:

  • Demonstrated superior communication efficiency and scalability of the proposed FLOACC approach in various FL scenarios.
  • Validated robust performance even in the presence of noisy channels.
  • Achieved significant enhancements in prediction accuracy and reduction in loss function for analog aggregation in over-the-air FL.

Conclusions:

  • The proposed method effectively mitigates channel noise in analog over-the-air federated learning, ensuring robust performance.
  • This approach offers a communication-efficient and scalable solution for time-sensitive industrial applications like predictive maintenance.
  • The findings highlight the potential of analog aggregation over-the-air for advancing edge learning in Industry 4.0 environments.