To address the increased latency, network load and compromised privacy issues associated with the Cloud-centric IoT applications, fog computing has emerged. Fog computing utilizes the proximal computational and storage devices, for sensor data analytics. The edge-fog-cloud continuum thus provides significant edge analytics capabilities for realizing interesting IoT applications. While edge analytics tasks are usually performed on a single node, distributed edge analytics proposes utilizing multiple nodes from the continuum, concurrently. This paper discusses and demonstrates distributed edge analytics from three different perspectives; serverless data pipelines (SDP), distributed computing and edge analytics, and federated learning, with our frameworks, MQTT based SDP, CANTO and FIDEL, respectively. The results produced in the paper, through different case studies, show the feasibility of performing distributed edge analytics following the three approaches, across the continuum.

翻译：为解决以云为中心的物联网应用所面临的延迟增加、网络负载加重及隐私受损等问题，雾计算应运而生。雾计算利用近端的计算与存储设备进行传感器数据分析。因此，边缘-雾-云计算连续体为实现各类有趣的物联网应用提供了强大的边缘分析能力。尽管边缘分析任务通常在单一节点上执行，分布式边缘分析则提出同时利用连续体中的多个节点。本文从三个不同视角探讨并展示了分布式边缘分析：无服务器数据流水线、分布式计算与边缘分析，以及联邦学习，并分别通过我们提出的基于MQTT的无服务器数据流水线、CANTO和FIDEL框架进行阐述。文中通过多个案例研究得出的结果表明，在连续体中采用这三种方法执行分布式边缘分析具有可行性。