Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.

翻译：联邦学习近期被应用于推荐系统以保护用户隐私。在联邦学习框架下，推荐系统无需收集真实用户数据，仅需收集中间参数即可训练推荐模型，从而显著增强用户隐私保护。此外，联邦推荐系统能在满足法规和隐私约束的前提下与其他数据平台协作，提升推荐模型性能。然而，联邦推荐系统面临隐私、安全、异构性和通信成本等多重新挑战。尽管这些领域已有大量研究，但文献综述仍存在空白。本综述将：(1)总结联邦推荐系统中常用的隐私保护机制，并讨论各机制的优缺点；(2)回顾若干鲁棒的聚合策略及针对安全性的新型攻击方法；(3)总结应对异构性和通信成本问题的若干方案；(4)介绍可用于构建联邦推荐系统的开源平台；(5)提出未来值得探索的研究方向。本综述可帮助研究人员和实践者理解这些领域的研究进展。