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Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models
  • Ameni Kallel ,
  • Molka Rekik ,
  • Mahdi Khemakhem
Ameni Kallel
Laboratoire OLID

Corresponding Author:[email protected]

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Molka Rekik
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Mahdi Khemakhem
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The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.
Apr 2022Published in The Journal of Supercomputing volume 78 issue 5 on pages 7078-7105. 10.1007/s11227-021-04166-9