B5G-based tactile edge learning shows promise as a solution to handle infectious diseases such as COVID-19 at a global level. By leveraging edge computing with the 5G RAN, management of epidemic diseases such as COVID-19 can be conducted efficiently. Deploying a hierarchical edge computing architecture offers several benefits such as scalability, low latency, and privacy for the data and the training model, which enables analysis of COVID-19 by a local trusted edge server. However, existing deep learning (DL) algorithms suffer from two crucial drawbacks: first, the training requires a large COVID-19 dataset on various dimensions, which is difficult for any local authority to manage. Second, the DL results require ethical approval and explanations from healthcare providers and other stakeholders in order to be accepted. In this article, we propose a B5G framework that supports COVID-19 diagnosis, leveraging the low-latency, high-bandwidth features of the 5G network at the edge. Our framework employs a distributed DL paradigm where each COVID-19 edge employs its own local DL framework and uses a three-phase reconciliation with the global DL framework. The local DL model runs on edge nodes while the global DL model runs on a cloud environment. The training of a local DL model is performed with the dataset available from the edge; it is applied to the global model after receiving approval from the subject matter experts at the edge. Our framework adds semantics to existing DL models so that human domain experts on COVID-19 can gain insight and semantic visualization of the key decision-making activities that take place within the deep learning ecosystem. We have implemented a subset of various COVID-19 scenarios using distributed DL at the edge and in the cloud. The test results are promising.

中文翻译：

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B5G和可解释的深度学习辅助医疗垂直服务：COVID-I9观点

基于B5G的触觉边缘学习显示出有望在全球范围内解决诸如COVID-19等传染病的解决方案。通过利用5G RAN进行边缘计算，可以有效地进行流行病（如COVID-19）的管理。部署分层边缘计算体系结构可带来多种好处，例如可伸缩性，低延迟以及数据和培训模型的私密性，从而使本地受信任边缘服务器能够分析COVID-19。但是，现有的深度学习（DL）算法具有两个关键缺陷：首先，训练需要在各个维度上都有大型COVID-19数据集，这对于任何本地机构而言都是难以管理的。其次，DL结果需要得到医疗保健提供者和其他利益相关者的道德认可和解释，才能被接受。在这篇文章中，我们提出了一个B5G框架，该框架利用边缘5G网络的低延迟，高带宽功能来支持COVID-19诊断。我们的框架采用分布式DL范式，其中每个COVID-19边缘均使用其自己的本地DL框架，并与全局DL框架进行三相协调。本地DL模型在边缘节点上运行，而全局DL模型在云环境上运行。本地DL模型的训练是使用可从边缘获得的数据集进行的；在获得边缘主题专家的批准后，它将应用于全局模型。我们的框架将语义添加到现有的DL模型中，以便COVID-19的人类领域专家可以获得深度学习生态系统内发生的关键决策活动的见解和语义可视化。我们已经在边缘和云中使用分布式DL实现了各种COVID-19方案的子集。测试结果很有希望。