In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing user data across domains, thereby violating the General Data Protection Regulation (GDPR). Consequently, numerous approaches have been proposed for Federated Cross-Domain Recommendation (FedCDR). Nevertheless, the data heterogeneity across different domains inevitably influences the overall performance of federated learning. In this study, we propose FedHCDR, a novel Federated Cross-Domain Recommendation framework with Hypergraph signal decoupling. Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features. The approach employs high-pass and low-pass hypergraph filters to decouple domain-exclusive and domain-shared user representations, which are trained by the local-global bi-directional transfer algorithm. In addition, a hypergraph contrastive learning (HCL) module is devised to enhance the learning of domain-shared user relationship information by perturbing the user hypergraph. Extensive experiments conducted on three real-world scenarios demonstrate that FedHCDR outperforms existing baselines significantly.

翻译：近年来，跨域推荐（Cross-Domain Recommendation, CDR）利用多个领域的用户数据提升推荐性能，受到了广泛关注。然而，现有CDR方法需要跨域共享用户数据，从而违反了《通用数据保护条例》（GDPR）。为此，众多研究提出了联邦跨域推荐（Federated Cross-Domain Recommendation, FedCDR）方法。然而，不同领域间的数据异质性不可避免地影响了联邦学习的整体性能。本研究提出了一种名为 FedHCDR 的新型联邦跨域推荐框架，其核心为超图信号解耦。具体而言，为应对跨域数据异质性，我们引入了一种称为超图信号解耦（Hypergraph Signal Decoupling, HSD）的方法，将用户特征解耦为领域专属特征与领域共享特征。该方法采用高通和低通超图滤波器，分别解耦领域专属和领域共享的用户表示，并通过局部-全局双向迁移算法进行训练。此外，我们设计了一个超图对比学习（Hypergraph Contrastive Learning, HCL）模块，通过对用户超图进行扰动，增强对领域共享用户关系信息的学习。在三个真实场景上进行的广泛实验表明，FedHCDR 显著优于现有基线方法。