Federated recommendation facilitates collaborative model training across distributed clients while keeping sensitive user interaction data local. Conventional approaches typically rely on synchronizing high-dimensional item representations between the server and clients. This paradigm implicitly assumes that precise geometric alignment of embedding coordinates is necessary for collaboration across clients. We posit that establishing relative semantic relationships among items is more effective than enforcing shared representations. Specifically, global semantic relations serve as structural constraints for items. Within these constraints, the framework allows item representations to vary locally on each client, which flexibility enables the model to capture fine-grained user personalization while maintaining global consistency. To this end, we propose Cluster-Guided FedRec framework (CGFedRec), a framework that transforms uploaded embeddings into compact cluster labels. In this framework, the server functions as a global structure discoverer to learn item clusters and distributes only the resulting labels. This mechanism explicitly cuts off the downstream transmission of item embeddings, relieving clients from maintaining global shared item embeddings. Consequently, CGFedRec achieves the effective injection of global collaborative signals into local item representations without transmitting full embeddings. Extensive experiments demonstrate that our approach significantly improves communication efficiency while maintaining superior recommendation accuracy across multiple datasets.

翻译：联邦推荐通过在分布式客户端间协同训练模型，同时将敏感的用户交互数据保留在本地。传统方法通常依赖于服务器与客户端之间同步高维物品表征。该范式隐含地假设嵌入坐标的精确几何对齐是实现跨客户端协作的必要条件。我们提出，建立物品间的相对语义关系比强制共享表征更为有效。具体而言，全局语义关系可作为物品的结构约束。在此约束下，该框架允许物品表征在每个客户端本地自由变化，这种灵活性使模型能够在保持全局一致性的同时捕捉细粒度的用户个性化特征。为此，我们提出聚类引导的联邦推荐框架（CGFedRec），该框架将上传的嵌入转换为紧凑的聚类标签。在此框架中，服务器作为全局结构发现者学习物品聚类，并仅分发生成的标签。该机制显式地切断了物品嵌入的下游传输，使客户端无需维护全局共享的物品嵌入。因此，CGFedRec能够在无需传输完整嵌入的情况下，将全局协同信号有效注入本地物品表征。大量实验表明，我们的方法在保持多个数据集上优异推荐精度的同时，显著提升了通信效率。