联邦学习作为解决推荐系统隐私安全问题的主流框架，在实际应用中却面临推荐精度欠佳、隐私保护力度不足及通信开销过大的问题。针对这些问题，本文提出一种融合图神经网络与深度图聚类的联邦推荐算法。首先，利用图神经网络对复杂的用户-项目的高阶交互关系进行捕捉，以提升推荐系统的推荐精度；其次，在联邦学习客户端与服务器端的通信环节注入差分隐私噪声以模糊真实梯度，进而增强推荐系统的隐私保护能力；最后，通过引入深度图聚类对客户端实施聚类，选取各簇的客户端代表参与训练，并将所得参数在簇内共享，以加快模型收敛速度，降低联邦学习框架下的通信开销。基于真实数据集的实验结果表明，所提算法在提高推荐精度的同时，能够增强系统的隐私保护力度并减少通信开销。
Federated learning, as the main framework for addressing privacy and security issues in recommendation systems, faces problems such as poor recommendation accuracy, insufficient privacy protection, and excessive communication overhead in practical applications. To address these issues, this paper proposed a federated recommendation algorithm integrating graph neural networks and depth graph clustering. Firstly, a graph neural network was used to capture high-order complex user and item interaction relationships, improving the recommendation accuracy of the recommendation system. Secondly, differential privacy noise was injected into the communication link between the federated learning clients and server to blur the true gradient, thereby enhancing the privacy protection capability of the recommendation system. Finally, clients were clustered by introducing depth graph clustering, and client representatives from each cluster were selected to participate in training. The obtained parameters were shared within the cluster to accelerate the convergence speed of the model and reduce communication overhead under the federated learning framework. The experimental results on real datasets show that the proposed algorithm can enhance privacy protection of the system and reduce communication overhead while improving recommendation accuracy.
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