Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

中文翻译：

---

测量和应用：探索传感器应用中分层联邦学习的挑战和机遇

随着数字时代的不断发展，传感器应用在现代社会中已变得无处不在。基于人工智能的技术（例如机器学习）可以有效地从大量数据中提取可操作的信息。一个例子是自动灌溉系统，该系统使用基于人工智能的土壤质量数据技术来决定如何最好地分配水。然而，这些基于人工智能的技术在硬件资源方面成本高昂，而物联网 (IoT) 传感器在处理能力、能源和存储容量方面受到资源限制。这些限制可能会损害传感器驱动应用程序的安全性、性能和可靠性。为了解决这些问题，传感器应用程序可以使用云计算服务进行数据存储和处理。不幸的是，需要实时处理的基于云的传感器应用程序，例如医疗应用程序（例如跌倒检测和中风预测），由于传感器节点和云之间的网络稀疏且不可靠，很容易受到网络延迟等问题的影响。服务器[1]。随着用户接近通信网络的边缘，延迟问题变得更加严重和频繁。一个有前途的替代方案是边缘计算，它通过将存储和处理能力从集中节点推送到更靠近数据收集位置的边缘设备，在网络边缘提供类似云的功能，从而减少网络延迟[2]， [3]。