  To enhance the quality and speed of data processing and protect the privacy
and security of the data, edge computing has been extensively applied to
support data-intensive intelligent processing services at edge. Among these
data-intensive services, ensemble learning-based services can in natural
leverage the distributed computation and storage resources at edge devices to
achieve efficient data collection, processing, analysis.
  Collaborative caching has been applied in edge computing to support services
close to the data source, in order to take the limited resources at edge
devices to support high-performance ensemble learning solutions. To achieve
this goal, we propose an adaptive in-network collaborative caching scheme for
ensemble learning at edge. First, an efficient data representation structure is
proposed to record cached data among different nodes. In addition, we design a
collaboration scheme to facilitate edge nodes to cache valuable data for local
ensemble learning, by scheduling local caching according to a summarization of
data representations from different edge nodes. Our extensive simulations
demonstrate the high performance of the proposed collaborative caching scheme,
which significantly reduces the learning latency and the transmission overhead.
