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arXiv AI By Jinxi Yu, Eric Hanchen Jiang, Levina Li, Dong Liu, Zhi Zhang, Wenxiao Zhao, Yanxuan Yu, Kai-Wei Chang, Ying Nian Wu

Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

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The paper introduces FGLGuard, a privacy‑preserving federated graph learning framework that trains a graph attention detector on each operator’s own multi‑agent system (MAS) episode graphs, sharing only model updates. By combining a proximal local objective, domain‑balanced aggregation, threshold calibration, and guarded rewrite mechanisms, FGLGuard adapts to non‑IID data across organizations and outperforms centralized and local‑only baselines on Agent‑SafetyBench, R‑Judge, and AgentDojo. The method achieves significant reductions in attack success rates—up to 43% on AgentDojo—without compromising utility, API cost, or model capability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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