Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose a Distributed COVID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines.

中文翻译：

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DisCOV：通过边缘云协作对 X 射线图像进行分布式 COVID-19 检测


当前，全球正在经历 2019 年冠状病毒病（COVID-19）的快速传播。由于该流行病继续对社会、经济和医疗保健造成毁灭性影响，因此实时检测 COVID-19 对于快速且经济高效的诊断服务至关重要。幸运的是，深度学习（DL）作为一项有前途的技术，可以在胸部 X 射线（CXR）图像上实现 COVID-19 诊断服务。 DL模型的训练任务一般在集中式云端实现。然而，由于数据源地理分散，且大量原始数据传输到集中式云端，传输延迟成为了COVID-19诊断模型训练的瓶颈。在本文中，我们提出了一种边云协作的 CXR 图像分布式 COVID-19 检测模型训练方法，名为 DisCOV。具体而言，为了提高训练效率并保证模型精度，结合边缘计算和云计算，设计了一种分布式轻量级基于模型的训练算法。此外，在训练过程中还开发了资源分配算法，以共同最小化时间成本和能耗。基于真实世界 CXR 图像数据集的大量实验表明，DisCOV 比现有基线性能更好、更有前景。