  This work introduces two strategies for training network classifiers with
heterogeneous agents. One strategy promotes global smoothing over the graph and
a second strategy promotes local smoothing over neighbourhoods. It is assumed
that the feature sizes can vary from one agent to another, with some agents
observing insufficient attributes to be able to make reliable decisions on
their own. As a result, cooperation with neighbours is necessary. However, due
to the fact that the feature dimensions are different across the agents, their
classifier dimensions will also be different. This means that cooperation
cannot rely on combining the classifier parameters. We instead propose
smoothing the outputs of the classifiers, which are the predicted labels. By
doing so, the dynamics that describes the evolution of the network classifier
becomes more challenging than usual because the classifier parameters end up
appearing as part of the regularization term as well. We illustrate performance
by means of computer simulations.
