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In this work, a novel approach to earthquake detection by integrating deep learning architectures with decentralized data management is introduced. To this end, variational autoencoders are trained within the OASEES framework, employing the InterPlanetary File System for data and model storage, moving beyond traditional centralized cloud/edge processing. The obtained experimental results demonstrate the model’s ability in classifying seismic events, achieving an accuracy level of \(97.24\%\) . The proposed distributed architecture not only achieves top performance, but also aligns with the heterogeneous cloud-fog-edge computing continuum, offering improved data governance and control.

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Utilizing Distributed Machine Learning Environments for Earthquake Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Alexandros S. Kalafatelis,
  • Charis Michailidis,
  • Georgios Alexandridis,
  • Andreas Oikonomakis,
  • Georgios Xylouris,
  • Eleni Smyrou,
  • Ihsan Bal,
  • Urtza Iturraspe Barturen,
  • Enrique Areizaga Sanchez,
  • Michail-Alexandros Kourtis,
  • Panagiotis Trakadas

摘要

In this work, a novel approach to earthquake detection by integrating deep learning architectures with decentralized data management is introduced. To this end, variational autoencoders are trained within the OASEES framework, employing the InterPlanetary File System for data and model storage, moving beyond traditional centralized cloud/edge processing. The obtained experimental results demonstrate the model’s ability in classifying seismic events, achieving an accuracy level of \(97.24\%\) . The proposed distributed architecture not only achieves top performance, but also aligns with the heterogeneous cloud-fog-edge computing continuum, offering improved data governance and control.