Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning
3.60T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2609.02967.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryFGLGuard is a federated graph learning framework for safety in LLM-based multi-agent systems. It trains a graph attention detector on episode graphs locally per organization, sharing only model updates, and outperforms centralized in-domain baselines on Agent-SafetyBench, R-Judge, and AgentDojo while cutting attack-success rate by 43% at near-unguarded utility.
Why it mattersCross-organization MAS safety with measurable federated-vs-centralized tradeoffs; relevant for anyone deploying multi-agent systems across trust boundaries.
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