  The recent decades have seen a surge of interests in distributed computing.
Existing work focus primarily on either distributed computing platforms, data
query tools, or, algorithms to divide big data and conquer at individual
machines etc. It is, however, increasingly often that the data of interest are
inherently distributed, i.e., data are stored at multiple distributed sites due
to diverse collection channels, business operations etc. We propose to enable
learning and inference in such a setting via a general framework based on the
distortion minimizing local transformations. This framework only requires a
small amount of local signatures to be shared among distributed sites,
eliminating the need of having to transmitting big data. Computation can be
done very efficiently via parallel local computation. The error incurred due to
distributed computing vanishes when increasing the size of local signatures. As
the shared data need not be in their original form, data privacy may also be
preserved. Experiments on linear (logistic) regression and Random Forests have
shown promise of this approach. This framework is expected to apply to a
general class of tools in learning and inference with the continuity property.
