Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing

Summary

This paper proposes HMP-GAE, a Hypergraph Message Passing Graph Autoencoder defense that defends federated LLM training against Byzantine attacks. It represents low-rank model updates as hypergraph nodes and suppresses semantically inconsistent updates using historical checkpoints and cross-agent relational reasoning. Evaluated on Qwen-2.5 and Llama-3.2 backbones with AG News and Yahoo! Answers, it improves global accuracy by 1.21%, reduces semantic entropy by 44.08%, and lowers perplexity by 13.34% relative to existing defenses.

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