  Graph neural networks (GNNs) fuel diverse machine learning tasks involving
graph-structured data, ranging from predicting protein structures to serving
personalized recommendations. Real-world graph data must often be stored
distributed across many machines not just because of capacity constraints, but
because of compliance with data residency or privacy laws. In such setups,
network communication is costly and becomes the main bottleneck to train GNNs.
Optimizations for distributed GNN training have targeted data-level
improvements so far -- via caching, network-aware partitioning, and
sub-sampling -- that work for data center-like setups where graph data is
accessible to a single entity and data transfer costs are ignored.
  We present RETEXO, the first framework which eliminates the severe
communication bottleneck in distributed GNN training while respecting any given
data partitioning configuration. The key is a new training procedure, lazy
message passing, that reorders the sequence of training GNN elements. RETEXO
achieves 1-2 orders of magnitude reduction in network data costs compared to
standard GNN training, while retaining accuracy. RETEXO scales gracefully with
increasing decentralization and decreasing bandwidth. It is the first framework
that can be used to train GNNs at all network decentralization levels --
including centralized data-center networks, wide area networks, proximity
networks, and edge networks.
