  Modern machine learning techniques are successfully being adapted to data
modeled as graphs. However, many real-world graphs are typically very large and
do not fit in memory, often making the problem of training machine learning
models on them intractable. Distributed training has been successfully employed
to alleviate memory problems and speed up training in machine learning domains
in which the input data is assumed to be independently identical distributed
(i.i.d). However, distributing the training of non i.i.d data such as graphs
that are used as training inputs in Graph Convolutional Networks (GCNs) causes
accuracy problems since information is lost at the graph partitioning
boundaries.
  In this paper, we propose a training strategy that mitigates the lost
information across multiple partitions of a graph through a subgraph
approximation scheme. Our proposed approach augments each sub-graph with a
small amount of edge and vertex information that is approximated from all other
sub-graphs. The subgraph approximation approach helps the distributed training
system converge at single-machine accuracy, while keeping the memory footprint
low and minimizing synchronization overhead between the machines.
