  A change of the prevalent supervised learning techniques is foreseeable in
the near future: from the complex, computational expensive algorithms to more
flexible and elementary training ones. The strong revitalization of randomized
algorithms can be framed in this prospect steering. We recently proposed a
model for distributed classification based on randomized neural networks and
hyperdimensional computing, which takes into account cost of information
exchange between agents using compression. The use of compression is important
as it addresses the issues related to the communication bottleneck, however,
the original approach is rigid in the way the compression is used. Therefore,
in this work, we propose a more flexible approach to compression and compare it
to conventional compression algorithms, dimensionality reduction, and
quantization techniques.
