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This research addresses a critical bottleneck in federated learning by leveraging frozen LLMs to generate semantic vectors that guide the alignment of structural graph embeddings across non-IID clients. By using these high-level knowledge signals, the framework enables meaningful cross-client collaboration without requiring raw data sharing or naive averaging techniques. The approach demonstrates consistent improvements in recommendation accuracy on standard benchmarks while maintaining strict privacy boundaries for distributed training environments.
Guiding Federated Graph Recommendation with LLM-encoded knowledge
arXiv:2606.15277v1 Announce Type: cross 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.
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.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Databases (cs.DB); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
arXiv:2606.15277 [cs.IR]
(or arXiv:2606.15277v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2606.15277 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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