FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense
Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches that lack semantic understanding, making them vulnerable to stealthy triggers and harmful to benign structures. To solve this, we present FedLSG, the first framework that integrates large language models (LLMs) into federated graph backdoor defense. FedLSG introduces a graph and behavior to text grounding scheme that transforms local graph structures and client update behaviors into semantically rich natural language representations. The framework further adopts a lightweight student-teacher architecture. On the server side, a full scale LLM ...
| Authors | Chenyu Zhou, Yabin Peng, Wei Huang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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