Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improve the accuracy and generalization of the model, while also protecting the privacy of user data. Graph-federated learning is mainly based on the classical federated learning framework i.e., the Client-Server framework. However, the Client-Server framework faces problems such as a single point of failure of the central server and poor scalability of network topology. First, we introduce the decentralized framework to graph-federated learning. Second, determine the confidence among nodes based on the similarity of data among nodes, subsequently, the gradient information is then aggregated by linear weighting based on confidence. Finally, the proposed method is compared with FedAvg, Fedprox, GCFL, and GCFL+ to verify the effectiveness of the proposed method. Experiments demonstrate that the proposed method outperforms other methods.

翻译：图学习在众多场景中具有广泛应用，这些场景对数据隐私提出了更高需求。联邦学习是一种新兴的分布式机器学习方法，它利用个体设备或数据中心的数据来提高模型的准确性和泛化能力，同时保护用户数据的隐私。图联邦学习主要基于经典的联邦学习框架，即客户端-服务器框架。然而，客户端-服务器框架面临中心服务器单点故障和网络拓扑可扩展性差等问题。首先，我们将去中心化框架引入图联邦学习。其次，基于节点间数据的相似性确定节点间的置信度，随后基于置信度对梯度信息进行线性加权聚合。最后，将所提方法与FedAvg、Fedprox、GCFL和GCFL+进行对比，以验证其有效性。实验表明，所提方法优于其他方法。