FedHUR：学习层次化效用引导的客户端关系用于个性化联邦推荐
FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation
- Fudan University（复旦大学）
- Microsoft Research Asia（微软亚洲研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FedHUR提出层次化效用引导的客户端关系学习方法，通过物品过滤器聚合和聚类本地信息，利用效用信号实现个性化联邦推荐，在五个数据集上超越现有基线。
AI中文摘要：
联邦推荐使得在保持用户交互数据位于本地客户端的同时进行协作模型训练成为可能。联邦推荐中的一个核心问题是如何跨客户端聚合有用信息以实现个性化推荐。现有的个性化聚合方法通常基于预定义的参数假设（如参数相似性或互补性）构建客户端关系，并利用这些关系来确定聚合权重。然而，此类方法构建的是单一的全局关系，不足以捕捉推荐中用户关系的层次性和多粒度特性。此外，这些预定义的关系无法直接反映相关客户端在聚合后能否提升预测性能。为解决这些局限性，我们提出了FedHUR，一个用于学习层次化效用引导客户端关系的联邦推荐框架。FedHUR将物品-物品过滤器作为关系构建和聚合的对象。具体而言，它首先聚合并聚类每个客户端的本地信息以获得全局层次化信息。每个客户端基于其本地信息和全局层次化信息计算层次化效用信号，指示哪些协作信息对其提升预测有用。服务器利用这些效用信号检索对该客户端有用的客户端，以进行进一步的个性化聚合。在五个真实世界数据集上的大量实验表明，FedHUR持续优于现有的联邦推荐基线，证明了层次化效用引导的客户端关系学习的有效性。代码可在该https URL获取。
英文摘要：
Federated recommendation enables collaborative model training while keeping user interaction data on local clients. A central problem in federated recommendation is how to aggregate useful information across clients for personalized recommendation. Existing personalized aggregation methods usually construct client relations from predefined parameter-based assumptions, such as parameter similarity or complementarity, and use these relations to determine aggregation weights. However, such methods construct a single global relation, which is insufficient to capture the hierarchical and multi-granularity nature of user relations in recommendation. Moreover, these predefined relations cannot directly reflect whether the related clients can improve prediction performance after aggregation. To address these limitations, we propose FedHUR, a federated recommendation framework for learning hierarchical utility-guided client relations. FedHUR takes item-item filters as the object for relation construction and aggregation. Specifically, it first aggregates and clusters each client's local information to obtain global hierarchical information. Each client computes hierarchical utility signals based on its local information and the global hierarchical information, indicating which collaborative information is useful for improving its prediction. The server uses these utility signals to retrieve clients that are useful to that client for further personalized aggregation. Extensive experiments on five real-world datasets show that FedHUR consistently outperforms existing federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning. Code is available at https://github.com/Mingzhe-Han/FedHUR.
