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LLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The introduction of LLM-Guided Dynamic-UMAP marks a significant advancement in personalized federated graph learning by leveraging large language models to enhance graph machine learning under privacy constraints. This method employs data augmentation and in-context learning to facilitate node classification and link prediction, particularly in low-resource environments. Its applications extend to knowledge graph completion and recommendation-style link prediction, demonstrating its versatility. Furthermore, the method incorporates a differential privacy threat model, ensuring user data protection while optimizing learning outcomes. This development aligns with ongoing efforts in the AI community to create more personalized and privacy-conscious machine learning solutions.
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