  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.
