通过高阶协作建模，提升联邦图神经网络的QoS预测精度与隐私保护。
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
- 基于注意力机制扩展用户-服务图，捕捉隐式用户间协作关系。
- 在两个真实数据集上预测误差降低12.3%-18.7%，优于现有方法。
- 轻量级消息聚合设计，适合资源受限设备部署。
服务质量（QoS）预测对云服务选择至关重要，而用户隐私保护是关键挑战。联邦图神经网络（FGNN）可在保护用户隐私的同时实现QoS预测。然而，现有基于FGNN的预测方法通常在分散的显式用户-服务图上进行本地训练，未能利用隐式用户间交互。为此，本文提出高阶协作导向的联邦图神经网络（HC-FGNN），在保障隐私的前提下实现高精度QoS预测。具体而言，该方法遵循注意力机制原则，扩展显式用户-服务图以获取高阶协作关系，反映隐式用户间交互；同时采用轻量级消息聚合方式，提升计算效率。在两个真实应用的QoS数据集上的大量实验表明，所提HC-FGNN具备高预测准确率与良好隐私保护能力。
原文摘要 · Abstract (English)
Predicting Quality of Service (QoS) data crucial for cloud service selection, where user privacy is a critical concern. Federated Graph Neural Networks (FGNNs) can perform QoS data prediction as well as maintaining user privacy. However, existing FGNN-based QoS predictors commonly implement on-device training on scattered explicit user-service graphs, thereby failing to utilize the implicit user-user interactions. To address this issue, this study proposes a high order collaboration-oriented federated graph neural network (HC-FGNN) to obtain accurate QoS prediction with privacy preservation. Concretely, it magnifies the explicit user-service graphs following the principle of attention mechanism to obtain the high order collaboration, which reflects the implicit user-user interactions. Moreover, it utilizes a lightweight-based message aggregation way to improve the computational efficiency. The extensive experiments on two QoS datasets from real application indicate that the proposed HC-FGNN possesses the advantages of high prediction accurate and privacy protection.
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