📝 SummarXiv

Abstract

Personalized federated recommendation system (FedRec) has gained significant attention for its ability to preserve privacy in delivering tailored recommendations. To alleviate the statistical heterogeneity challenges among clients and improve personalization, decoupling item embeddings into the server and client-specific views has become a promising way. Among them, the global item embedding table serves as a consensus representation that integrates and reflects the collective patterns across all clients. However, the inherent sparsity and high uniformity of interaction data from massive-scale clients results in degraded consensus and insufficient decoupling, reducing consensus's utility. To this end, we propose a \textbf{Fed}erated \textbf{C}onsistency- and \textbf{C}omplementarity-aware \textbf{C}onsensus-enhanced \textbf{R}ecommendation (Fed3CR) method for personalized FedRec. To improve the efficiency of the utilization of consensus, we propose an \textbf{A}daptive \textbf{C}onsensus \textbf{E}nhancement (ACE) strategy to learn the relationship between global and client-specific item embeddings. It enables the client to adaptively enhance specific information in the consensus, transforming it into a form that best suits itself. To improve the quality of decoupling, we propose a \textbf{C}onsistency- and \textbf{C}omplementarity-aware \textbf{O}ptimization (C2O) strategy, which helps to learn more effective and complementary representations. Notably, our proposed Fed3CR is a plug-and-play method, which can be integrated with other FedRec methods to improve its performance. Extensive experiments on four real-world datasets represent the superior performance of Fed3CR.