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Federated recommendation applies federated learning techniques inrecommendation systems to help protect user privacy by exchanging modelsinstead of raw user data between user devices and the central server. Due tothe heterogeneity in user's attributes and local data, attaining personalizedmodels is critical to help improve the federated recommendation performance. Inthis paper, we propose a Graph Neural Network based Personalized FederatedRecommendation (PerFedRec) framework via joint representation learning, userclustering, and model adaptation. Specifically, we construct a collaborativegraph and incorporate attribute information to jointly learn the representationthrough a federated GNN. Based on these learned representations, we clusterusers into different user groups and learn personalized models for eachcluster. Then each user learns a personalized model by combining the globalfederated model, the cluster-level federated model, and the user's fine-tunedlocal model. To alleviate the heavy communication burden, we intelligentlyselect a few representative users (instead of randomly picked users) from eachcluster to participate in training. Experiments on real-world datasets showthat our proposed method achieves superior performance over existing methods.

Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation