Guiding Federated Graph Recommendation with LLM-encoded knowledge

• URL: http://arxiv.org/abs/2606.15277v1
• Date: Sat, 13 Jun 2026 12:30:57 GMT
• Title: Guiding Federated Graph Recommendation with LLM-encoded knowledge
• Abstract summary: Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions.<n>We propose a novel framework that uses LLM-encoded knowledge to guide federated graph recommendation.<n>This enables semantically informed cross-client collaboration without exposing raw data.
• Score: 8.842921339414714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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