  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.
