Due to underlying privacy-sensitive information in user-item interaction data, the risk of privacy leakage exists in the centralized-training recommender system (RecSys). To this issue, federated learning, a privacy-oriented distributed computing paradigm, is introduced and promotes the crossing field “Federated Recommender System (FedRec).” Regarding data distribution characteristics, there are horizontal, vertical, and transfer variants, where horizontal FedRec (HFedRec) occupies a dominant position. User devices can personally participate in the horizontal federated architecture, making user-level privacy feasible. Therefore, we target the horizontal point and summarize existing works more elaborately than existing FedRec surveys. First, from the model perspective, we group them into different learning paradigms (e.g., deep learning and meta learning). Second, from the privacy perspective, privacy-preserving techniques are systematically organized (e.g., homomorphic encryption and differential privacy). Third, from the federated perspective, fundamental issues (e.g., communication and fairness) are discussed. Fourth, each perspective has detailed subcategories, and we specifically state their unique challenges with the observation of current progress. Finally, we figure out potential issues and promising directions for future research.

由于用户-项目交互数据中隐藏着隐私敏感信息，集中训练推荐系统（RecSys）存在隐私泄露的风险。针对这一问题，提出了联邦学习这种面向隐私的分布式计算范式，并推动了跨领域的“联邦推荐系统（FedRec）”。就数据分布特征而言，有水平、垂直和传输等变体，其中水平FedRec（HFedRec）占据主导地位。用户设备可以亲自参与水平联合架构，使用户级隐私成为可能。因此，我们以水平点为目标，比现有的 FedRec 调查更详细地总结了现有的工作。首先，从模型的角度来看，我们将它们分为不同的学习范式（例如深度学习和元学习）。其次，从隐私角度来看，隐私保护技术是系统组织的（例如同态加密和差分隐私）。第三，从联邦的角度，讨论基本问题（例如沟通和公平）。第四，每个观点都有详细的子类别，我们通过观察当前进展来具体阐述其独特的挑战。最后，我们找出未来研究的潜在问题和有希望的方向。