FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation

Summary

FedGraph is a graph-based aggregation framework for federated LLM fine-tuning that defends against backdoor attacks. It models clients as nodes in a dynamically constructed graph, using the structural consistency of malicious updates to separate them from benign ones and suppress them during aggregation. Experiments report attack success rates below 10%, 95.5% average detection accuracy, and a 2.33% false positive rate.

Mitigation

FedGraph, a graph-based aggregation framework that suppresses malicious client contributions during aggregation.

Topics

• Adversarial machine learning

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