  In edge computing scenarios, the distribution of data and collaboration of
workloads on different layers are serious concerns for performance, privacy,
and security issues. So for edge computing benchmarking, we must take an
end-to-end view, considering all three layers: client-side devices, edge
computing layer, and cloud servers. Unfortunately, the previous work ignores
this most important point. This paper presents the BenchCouncil's coordinated e
ort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models
four typical application scenarios: ICU Patient Monitor, Surveillance Camera,
Smart Home, and Autonomous Vehicle with the focus on data distribution and
workload collaboration on three layers. Edge AIBench is a part of the
open-source AIBench project, publicly available from
http://www.benchcouncil.org/AIBench/index.html. We also build an edge computing
testbed with a federated learning framework to resolve performance, privacy,
and security issues.
