Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions, and federated learning (FL) allows these models to be trained while preserving user privacy. However, aggregating graph representations across distributed, non-IID clients remains a challenge; structural embeddings learned locally often misalign, and naive averaging fails to capture meaningful cross-client relationships. Most existing federated graph methods rely exclusively on structural aggregation, neglecting the rich, global semantic context available in large language models (LLMs). In this paper, we propose a novel framework that uses LLM-encoded knowledge to guide federated graph recommendation. Specifically, clients learn structural representations from local graphs while simultaneously summarizing their typical interaction patterns into compact semantic vectors via a frozen LLM. The central server then uses these LLM-encoded semantic signals to discover related preference patterns across clients, guiding the selective aggregation of their structural representations. This enables semantically informed cross-client collaboration without exposing raw data. Extensive experiments on standard benchmarks show that guiding structural alignment with LLM-encoded knowledge consistently improves recommendation accuracy over existing federated graph baselines.

翻译：图推荐系统在从用户-物品交互中提取协同信号方面非常高效，而联邦学习（FL）允许在保护用户隐私的同时训练这些模型。然而，在分布式的、非独立同分布的客户端之间聚合图表示仍是一个挑战；局部学习的结构嵌入往往存在错位，而简单平均无法捕捉有意义的跨客户端关系。现有的大多数联邦图方法仅依赖结构聚合，忽略了大型语言模型（LLMs）中蕴含的丰富全局语义上下文。在本文中，我们提出了一种新颖框架，利用LLM编码的知识来指导联邦图推荐。具体而言，客户端从本地图学习结构表示，同时通过冻结的LLM将其典型交互模式总结为紧凑的语义向量。中央服务器随后利用这些LLM编码的语义信号来发现跨客户端的相关偏好模式，指导其结构表示的选择性聚合。这实现了基于语义信息的跨客户端协作，而无需暴露原始数据。在标准基准上的大量实验表明，用LLM编码知识指导结构对齐，在推荐准确性上持续优于现有的联邦图基线方法。