Nowadays, user privacy is becoming an issue that cannot be bypassed for system developers, especially for that of web applications where data can be easily transferred through internet. Thankfully, federated learning proposes an innovative method to train models with distributed devices while data are kept in local storage. However, unlike general neural networks, although graph-based networks have achieved great success in classification tasks and advanced recommendation system, its high performance relies on the rich context provided by a graph structure, which is vulnerable when data attributes are incomplete. Therefore, the latter becomes a realistic problem when implementing federated learning for graph-based networks. Knowing that data embedding is a representation in a different space, we propose our Federated Graph-based Networks with Shared Embedding (Feras), which uses shared embedding data to train the network and avoids the direct sharing of original data. A solid theoretical proof of the convergence of Feras is given in this work. Experiments on different datasets (PPI, Flickr, Reddit) are conducted to show the efficiency of Feras for centralized learning. Finally, Feras enables the training of current graph-based models in the federated learning framework for privacy concern.

本研究提出了一种基于图嵌入的联邦学习方法，在不直接分享原始数据的情况下利用共享嵌入数据提高了现有基于图的网络的训练效率，并在不同数据集上进行了实验以显示其有效性，使得当前基于图的模型在联邦学习框架中的训练具备隐私保护的功能。

共享嵌入的联邦图神经网络