Graph convolutional networks (GCNs) have been widely adopted for graph
representation learning and achieved impressive performance. For larger graphs
stored separately on different clients, distributed GCN training algorithms
were proposed to improve efficiency and scalability. However, existing methods
directly exchange node features between different clients, which results in
data privacy leakage. Federated learning was incorporated in graph learning to
tackle data privacy, while they suffer from severe performance drop due to
non-iid data distribution. Besides, these approaches generally involve heavy
communication and memory overhead during the training process. In light of
these problems, we propose a Privacy-Preserving Subgraph sampling based
distributed GCN training method (PPSGCN), which preserves data privacy and
significantly cuts back on communication and memory overhead. Specifically,
PPSGCN employs a star-topology client-server system. We firstly sample a local
node subset in each client to form a global subgraph, which greatly reduces
communication and memory costs. We then conduct local computation on each
client with features or gradients of the sampled nodes. Finally, all clients
securely communicate with the central server with homomorphic encryption to
combine local results while preserving data privacy. Compared with federated
graph learning methods, our PPSGCN model is trained on a global graph to avoid
the negative impact of local data distribution. We prove that our PPSGCN
algorithm would converge to a local optimum with probability 1. Experiment
results on three prevalent benchmarks demonstrate that our algorithm
significantly reduces communication and memory overhead while maintaining
desirable performance. Further studies not only demonstrate the fast
convergence of PPSGCN, but discuss the trade-off between communication and
local computation cost as well.

本文提出了一种基于隐私保护子图采样的分布式图卷积网络训练方法（PPSGCN），它避免了现有方法中直接交换不同客户端之间节点特征导致的数据隐私泄露问题，并且通过采样局部节点子集形成全局子图方式大大降低了通信和内存成本，在保护数据隐私的前提下具有更加优异的性能表现。