AlignFedRec: Dual structural alignment for item representation learning in federated recommendation

Federated recommendation has emerged as a promising privacy-preserving paradigm for collaborative filtering. Most existing methods rely on embedding or parameter aggregation to share collaboration signals, implicitly assuming that representation spaces across clients are semantically consistent. However, due to the inherent statistical heterogeneity of user preferences, such aggregation inevitably suffers from representation drift and collaborative information loss. To address this issue, we propose AlignFedRec, a novel federated recommendation framework that shifts federated collaboration from parameter consistency to structural alignment. Instead of directly aggregating item embeddings, clients share privacy-preserving item relational structures, which serve as transferable collaborative information across heterogeneous clients. Based on this insight, we propose a dual structural alignment mechanism comprising item cluster alignment and global structural alignment, which aligns local item relational structures from both coarse-grained and fine-grained perspectives. Specifically, the item cluster alignment combines item community detection with supervised contrastive learning to enforce cluster-level structural consistency. Furthermore, the global structural alignment preserves the consistency between local and global item relational structures, thereby capturing fine-grained global correlations. Extensive experiments on multiple real-world datasets demonstrate that AlignFedRec consistently outperforms state-of-the-art federated recommendation baselines, validating its effectiveness in mitigating representation drift and boosting recommendation performance.