Collaborative filtering (CF) based recommender systems are typically trained
based on personal interaction data (e.g., clicks and purchases) that could be
naturally represented as ego graphs. However, most existing recommendation
methods collect these ego graphs from all users to compose a global graph to
obtain high-order collaborative information between users and items, and these
centralized CF recommendation methods inevitably lead to a high risk of user
privacy leakage. Although recently proposed federated recommendation systems
can mitigate the privacy problem, they either restrict the on-device local
training to an isolated ego graph or rely on an additional third-party server
to access other ego graphs resulting in a cumbersome pipeline, which is hard to
work in practice. In addition, existing federated recommendation systems
require resource-limited devices to maintain the entire embedding tables
resulting in high communication costs.
In light of this, we propose a semi-decentralized federated ego graph
learning framework for on-device recommendations, named SemiDFEGL, which
introduces new device-to-device collaborations to improve scalability and
reduce communication costs and innovatively utilizes predicted interacted item
nodes to connect isolated ego graphs to augment local subgraphs such that the
high-order user-item collaborative information could be used in a
privacy-preserving manner. Furthermore, the proposed framework is
model-agnostic, meaning that it could be seamlessly integrated with existing
graph neural network-based recommendation methods and privacy protection
techniques. To validate the effectiveness of the proposed SemiDFEGL, extensive
experiments are conducted on three public datasets, and the results demonstrate
the superiority of the proposed SemiDFEGL compared to other federated
recommendation methods.

本文提出了一种半去中心化的联邦图神经网络推荐框架（SemiDFEGL），它通过设备之间的合作来提高可扩展性并降低通信成本，并创新地利用预测的互动项目节点来连接孤立的 Ego 图以增强本地子图，进而以隐私保护的方式使用高阶用户 - 项协同信息。此外，该框架面向模型，可以与现有的图神经网络推荐方法和隐私保护技术无缝集成。实验结果表明，与其他联邦推荐方法相比，所提出的 SemiDFEGL 具有较高的性能优势。