FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation

2026-09-10

AI Summary

本文提出FedHUR框架，通过学习层次化的客户端关系来改进个性化联邦推荐问题，利用物品过滤器和效用信号实现更有效的信息聚合。

Abstract

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.

Problem

• federated recommendation
• personalized aggregation
• client relations
• hierarchical information
• utility signals

Innovation

• Hierarchical Utility-Guided Client Relations
• Federated Recommendation
• Personalized Aggregation
• Item-Item Filters
• Utility Signals