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.

翻译：图神经网络（GNN）驱动着涉及图结构数据的多样化机器学习任务，从预测蛋白质结构到提供个性化推荐。现实世界的图数据通常需要跨多台机器分布式存储，这不仅是出于容量限制，还因为要遵守数据驻留或隐私法律。在此类设置中，网络通信成本高昂并成为训练GNN的主要瓶颈。目前针对分布式GNN训练的优化主要聚焦于数据层面的改进——通过缓存、网络感知分区和子采样——这些优化适用于数据中心式设置，即图数据可由单一实体访问且忽略数据传输成本。我们提出RETEXO，这是首个在尊重任意给定数据分区配置的同时消除分布式GNN训练中严重通信瓶颈的框架。其关键在于一种新的训练流程——惰性消息传递——它重新排列了GNN元素的训练顺序。与标准GNN训练相比，RETEXO在网络数据成本上实现了1-2个数量级的降低，同时保持准确性。RETEXO能够优雅地适应日益增强的去中心化和不断降低的带宽。它是首个可在所有网络去中心化级别（包括集中式数据中心网络、广域网、邻近网络和边缘网络）上训练GNN的框架。