Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks

TL;DR

This paper introduces LLCG, a communication-efficient distributed GNN training algorithm that trains locally and corrects globally, reducing communication costs and memory overhead while maintaining high performance.

Contribution

The paper proposes LLCG, a novel distributed GNN training method that combines local training with global correction to improve scalability and efficiency.

Findings

LLCG reduces communication and memory overhead in distributed GNN training.

Global server corrections improve the performance of locally trained models.

Experiments show LLCG achieves high accuracy with lower resource consumption.

Abstract

Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependency between nodes in a graph, and the privacy concern due to the centralized storage and model learning have spurred the need to design an effective distributed algorithm for GNN training. However, existing distributed GNN training methods impose either excessive communication costs or large memory overheads that hinders their scalability. To overcome these issues, we propose a communication-efficient distributed GNN training technique named $\text{{Learn Locally, Correct Globally}}$ (LLCG). To reduce the communication and memory overhead, each local machine in LLCG first trains a GNN on its local data by ignoring the dependency between nodes among different machines, then sends the locally trained model to the server for periodic model averaging. However, ignoring node dependency could result in significant performance degradation. To solve the performance degradation, we propose to apply $\text{{Global Server Corrections}}$ on the server to refine the locally learned models. We rigorously analyze the convergence of distributed methods with periodic model averaging for training GNNs and show that naively applying periodic model averaging but ignoring the dependency between nodes will suffer from an irreducible residual error. However, this residual error can be eliminated by utilizing the proposed global corrections to entail fast convergence rate. Extensive experiments on real-world datasets show that LLCG can significantly improve the efficiency without hurting the performance.