LLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning

TL;DR

This paper introduces a novel LLM-guided approach for personalized federated graph learning that enhances graph tasks with data augmentation, prompt tuning, and dynamic embedding alignment, supporting low-resource scenarios.

Contribution

It presents a new method combining large language models with dynamic UMAP for personalized federated graph learning, including privacy considerations and diverse applications.

Findings

Supports node classification and link prediction in low-resource settings

Aligns language model representations with graph structures effectively

Provides a convergence argument and privacy analysis for the proposed method

Abstract

We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for sparse graphs, prompt and instruction tuning to adapt foundation models to graph tasks, and in-context learning to supply few-shot graph reasoning signals. These signals parameterize a Dynamic UMAP manifold of client-specific graph embeddings inside a Bayesian variational objective for personalized federated learning. The method supports node classification and link prediction in low-resource settings and aligns language model latent representations with graph structure via a cross-modal regularizer. We outline a convergence argument for the variational aggregation procedure, describe a differential privacy threat model based on a moments accountant, and present applications to knowledge graph completion, recommendation-style link prediction, and citation and product graphs. We also discuss evaluation considerations for benchmarking LLM-assisted graph machine learning.