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.

这项研究提出了一种名为 FedHCDR 的新型分布式跨领域推荐框架，通过超图信号解耦来解决不同领域数据异构性的问题，并且通过超图对于领域共享用户关系信息的学习来提升推荐性能。实验结果表明，FedHCDR 在三个真实场景中明显优于现有基准模型。

FedHCDR：超图信号解耦的联邦跨领域推荐

FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal  Decoupling

Privacy preservation and the protection of speech data is in high demand, not
least as a result of recent regulation, e.g. the General Data Protection
Regulation (GDPR) in the EU. While there has been a period with which to
prepare for its implementation, its implications for speech data is poorly
understood. This assertion applies to both the legal and technology
communities, and is hardly surprising since there is no universal definition of
'privacy', let alone a clear understanding of when or how the GDPR applies to
the capture, storage and processing of speech data. In aiming to initiate the
discussion that is needed to establish a level of harmonisation that is thus
far lacking, this contribution presents some reflections of both legal and
technology communities on the implications of the GDPR as regards speech data.
The article outlines the need for taxonomies at the intersection of speech
technology and data privacy - a discussion that is still very much in its
infancy - and describes the ways to safeguards and priorities for future
research. In being agnostic to any specific application, the treatment should
be of interest to the speech communication community at large.

本文旨在启动关于语音数据保护的讨论并提出了安排和优先考虑未来研究的建议，讨论了语音技术和数据隐私交叉点处的分类学的需求以及 GDPR 对语音数据的影响问题。