The development of 5G and artificial intelligence technologies have brought novel ideas to the prevention, control, and diagnosis of disease. Due to the limitation of the privacy protection of medical big data, releasing the data of patients is not allowed. However, as COVID-19 spreads globally, it is urgent to develop a robust diagnostic model to serve as many institutions as possible. Therefore, we propose a 5G-enabled architecture of auxiliary diagnosis based on federated learning for multiple institutions and central cloud collaboration to realize the sharing of diagnosis models with high generalization performance. In order to exchange model and parameters between the central and distributed nodes, a framework of diagnosis model cognition is constructed for sharing and updating the model adaptively. The severity classification experiments of COVID-19 were carried out on the central cloud and three edge cloud servers to verify the effectiveness of the proposed architecture and model cognition strategy. At the same time, the aggregated model shows good performance with accuracy rates of 95.3, 79.4, and 97.7 percent on distributed nodes, and the recognition results can assist doctors in executing auxiliary diagnosis. Finally, the open issues of model fusion of multimodal data in the 5G network architecture are discussed.

中文翻译：

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基于 5G 联邦学习的 COVID-19 辅助诊断

5G和人工智能技术的发展为疾病的预防、控制和诊断带来了新的思路。由于医疗大数据隐私保护的限制，不允许发布患者数据。但是，随着 COVID-19 在全球范围内传播，迫切需要开发一种强大的诊断模型来为尽可能多的机构提供服务。因此，我们提出了一种基于联邦学习的多机构联合学习和中心云协同的5G辅助诊断架构，以实现具有高泛化性能的诊断模型的共享。为了在中心节点和分布式节点之间交换模型和参数，构建了诊断模型认知框架，自适应地共享和更新模型。在中央云和三个边缘云服务器上进行了 COVID-19 的严重性分类实验，以验证所提出的架构和模型认知策略的有效性。同时，聚合模型在分布式节点上表现出良好的性能，准确率分别为95.3%、79.4%和97.7%，识别结果可以辅助医生进行辅助诊断。最后，讨论了 5G 网络架构中多模态数据模型融合的开放性问题。识别结果可以辅助医生进行辅助诊断。最后，讨论了 5G 网络架构中多模态数据模型融合的开放性问题。识别结果可以辅助医生进行辅助诊断。最后，讨论了 5G 网络架构中多模态数据模型融合的开放性问题。