Federated Learning is a promising approach for training machine learning models while preserving data privacy, but its distributed nature makes it vulnerable to backdoor attacks, particularly in NLP tasks while related research remains limited. This paper introduces SDBA, a novel backdoor attack mechanism designed for NLP tasks in FL environments. Our systematic analysis across LSTM and GPT-2 models identifies the most vulnerable layers for backdoor injection and achieves both stealth and long-lasting durability through layer-wise gradient masking and top-k% gradient masking within these layers. Experiments on next token prediction and sentiment analysis tasks show that SDBA outperforms existing backdoors in durability and effectively bypasses representative defense mechanisms, with notable performance in LLM such as GPT-2. These results underscore the need for robust defense strategies in NLP-based FL systems.

本研究针对联邦学习中的后门攻击问题，提出了一种新颖的后门攻击机制SDBA，特别针对自然语言处理任务。通过对LSTM和GPT-2模型的系统分析，识别了最脆弱的层并采用了层级梯度掩蔽和top-k%梯度掩蔽方法，实现了隐蔽性与持久性的结合。实验证明，SDBA在持久性和绕过代表性防御机制方面表现优越，强调了在基于NLP的联邦学习系统中需要加强防御策略的必要性。

一种隐蔽且持久的持久后门攻击机制在联邦学习中的应用