  Data are often accommodated on centralized storage servers. This is the case,
for instance, in remote sensing and astronomy, where projects produce several
petabytes of data every year. While machine learning models are often trained
on relatively small subsets of the data, the inference phase typically requires
transferring significant amounts of data between the servers and the clients.
In many cases, the bandwidth available per user is limited, which then renders
the data transfer to be one of the major bottlenecks. In this work, we propose
a framework that automatically selects the relevant parts of the input data for
a given neural network. The model as well as the associated selection masks are
trained simultaneously such that a good model performance is achieved while
only a minimal amount of data is selected. During the inference phase, only
those parts of the data have to be transferred between the server and the
client. We propose both instance-independent and instance-dependent selection
masks. The former ones are the same for all instances to be transferred,
whereas the latter ones allow for variable transfer sizes per instance. Our
experiments show that it is often possible to significantly reduce the amount
of data needed to be transferred without affecting the model quality much.
