Abstract: 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.

Key words: recommendation system, federated learning, privacy protection, depth graph clustering, graph neural network