SAIA：面向移动医疗系统的分离式人工智能架构
SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System
摘要：
随着深度学习、物联网和云计算技术在生物医学和医疗保健领域的应用，移动医疗系统受到了前所未有的关注。由于DL技术通常需要大量的计算，因此它们中的大多数不能直接部署在资源受限的移动和物联网设备上。因此，大多数移动医疗保健系统利用云计算基础设施，其中由移动和物联网设备收集的数据将被传输到云计算平台进行分析。然而，在竞争激烈的环境中，依赖云计算可能并不总是可行的。例如，卫星通信可能被拒绝或中断。我们提出了SAIA，一个分裂的人工智能架构的移动医疗保健系统。与传统的人工智能（AI）方法不同，SAIA不仅可以在无线通信可用的情况下依赖云计算基础设施，还可以利用在客户端本地工作的轻量级AI解决方案，因此，即使在通信受阻时也可以工作。在SAIA中，我们提出了一个基于元信息的决策单元，它可以调整客户端捕获的样本是否应该由嵌入式AI操作（即，保持在客户端上）或联网的AI（即，发送到服务器），在不同条件下。在我们的实验评估中，对两个流行的医疗数据集进行了广泛的实验。我们的研究结果表明，SAIA在有效性和效率方面始终优于其基线。
英文摘要：
As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems, mobile healthcare systems have received unprecedented attention. Since DL techniques usually require enormous amount of computation, most of them cannot be directly deployed on the resource-constrained mobile and IoT devices. Hence, most of the mobile healthcare systems leverage the cloud computing infrastructure, where the data collected by the mobile and IoT devices would be transmitted to the cloud computing platforms for analysis. However, in the contested environments, relying on the cloud might not be practical at all times. For instance, the satellite communication might be denied or disrupted. We propose SAIA, a Split Artificial Intelligence Architecture for mobile healthcare systems. Unlike traditional approaches for artificial intelligence (AI) which solely exploits the computational power of the cloud server, SAIA could not only relies on the cloud computing infrastructure while the wireless communication is available, but also utilizes the lightweight AI solutions that work locally on the client side, hence, it can work even when the communication is impeded. In SAIA, we propose a meta-information based decision unit, that could tune whether a sample captured by the client should be operated by the embedded AI (i.e., keeping on the client) or the networked AI (i.e., sending to the server), under different conditions. In our experimental evaluation, extensive experiments have been conducted on two popular healthcare datasets. Our results show that SAIA consistently outperforms its baselines in terms of both effectiveness and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
