Graph neural networks (GNN) have been widely deployed in real-world networked applications and systems due to their capability to handle graph-structured data. However, the growing awareness of data privacy severely challenges the traditional centralized model training paradigm, where a server holds all the graph information. Federated learning is an emerging collaborative computing paradigm that allows model training without data centralization. Existing federated GNN studies mainly focus on systems where clients hold distinctive graphs or sub-graphs. The practical node-level federated situation, where each client is only aware of its direct neighbors, has yet to be studied. In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs. We first design a tree constructor to improve the representation capability given the limited structural information. We further present a Monte Carlo Markov Chain-based algorithm to mitigate the workload imbalance caused by degree heterogeneity with theoretically-guaranteed performance. Based on the constructed tree for each client, a decentralized tree-based GNN trainer is proposed to support versatile training. Extensive experiments demonstrate that Lumos outperforms the baseline with significantly higher accuracy and greatly reduced communication cost and training time.

翻译：图神经网络（GNN）因其处理图结构数据的能力，已广泛应用于现实世界中的网络化应用与系统。然而，日益增长的数据隐私意识严重挑战了传统的集中式模型训练范式——即由单一服务器持有全部图信息。联邦学习作为一种新兴的协作计算范式，可在无需数据集中化的前提下进行模型训练。现有联邦GNN研究主要集中于客户端持有不同子图或全图的场景，而实际场景中每个客户端仅感知其直接邻居的节点级联邦问题尚未得到充分研究。本文首次提出名为Lumos的联邦GNN框架，该框架支持在节点级联邦图上实现特征与度数保护的有监督与无监督学习。我们首先设计树构造器以提升有限结构信息下的表示能力，进而提出基于蒙特卡洛马尔可夫链的算法，在理论保证性能的前提下缓解度数异构性带来的工作负载不均衡问题。基于各客户端构造的树，进一步提出去中心化树状GNN训练器以支持多样化训练。大量实验表明，Lumos在显著提升准确率的同时，大幅降低了通信开销与训练时间，性能全面优于基准方法。