DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs

• URL: http://arxiv.org/abs/2601.16519v1
• Date: Fri, 23 Jan 2026 07:32:54 GMT
• Title: DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
• Authors: Zekai Chen, Haodong Lu, Xunkai Li, Henan Sun, Jia Li, Hongchao Qin, Rong-Hua Li, Guoren Wang,
• Abstract summary: Text-attributed graph federated learning (TAG-FGL) improves graph learning by explicitly leveraging large language models (LLMs)<n>Current TAG-FGL methods face three main challenges: textbf(1) Overhead. LLMs for processing long texts incur high token and computation costs.<n>We propose textbfDANCE, a new TAG-FGL paradigm with GC.
• Score: 41.015941863230346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. With the rise of large language models (LLMs), textual attributes in FGL graphs are gaining attention. Text-attributed graph federated learning (TAG-FGL) improves FGL by explicitly leveraging LLMs to process and integrate these textual features. However, current TAG-FGL methods face three main challenges: \textbf{(1) Overhead.} LLMs for processing long texts incur high token and computation costs. To make TAG-FGL practical, we introduce graph condensation (GC) to reduce computation load, but this choice also brings new issues. \textbf{(2) Suboptimal.} To reduce LLM overhead, we introduce GC into TAG-FGL by compressing multi-hop texts/neighborhoods into a condensed core with fixed LLM surrogates. However, this one-shot condensation is often not client-adaptive, leading to suboptimal performance. \textbf{(3) Interpretability.} LLM-based condensation further introduces a black-box bottleneck: summaries lack faithful attribution and clear grounding to specific source spans, making local inspection and auditing difficult. To address the above issues, we propose \textbf{DANCE}, a new TAG-FGL paradigm with GC. To improve \textbf{suboptimal} performance, DANCE performs round-wise, model-in-the-loop condensation refresh using the latest global model. To enhance \textbf{interpretability}, DANCE preserves provenance by storing locally inspectable evidence packs that trace predictions to selected neighbors and source text spans. Across 8 TAG datasets, DANCE improves accuracy by \textbf{2.33\%} at an \textbf{8\%} condensation ratio, with \textbf{33.42\%} fewer tokens than baselines.

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