在语义通信中通过安全的联合微调来同步基于LLM的语义知识库
Long Li1, Yuanhang He2, Rui Xu1
1Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China.
Frontiers in artificial intelligence
|November 10, 2025
概括
一个新的安全联邦微调 (SecFFT) 方案通过保护大型语言模型知识库来增强语义通信. 它可以防止隐私泄露和毒害攻击,确保准确可靠的同步.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 语义通信 (SemCom) 对智能产业至关重要,它依赖于同步的语义知识库 (SKB).
- 大型语言模型 (LLM) 是构建这些SKB的关键,但联合微调引入了隐私泄露和中毒攻击等安全漏洞.
研究的目的:
- 提出一种新的安全联合微调 (SecFFT) 方案,用于在SemCom中同步基于LLM的SKB.
- 为了解决安全威胁,包括隐私泄露和毒害攻击在联邦更新期间.
主要方法:
- 整合同态加密以实现安全的参数同步.
- 实施基于哈希值的消息身份验证代码的基于剩余的访问控制机制,以应对中毒攻击.
- 设计一种自我适应的本地更新策略,以减轻毒性参数对良性参与者的影响.
主要成果:
- SecFFT安全地同步基于LLM的分布式SKB.
- 该方案保持了高准确度,达到联合LoRA的98.4%的性能.
- 在四个GLUE基准数据集上的实验验证证证了有效性.
结论:
- SecFFT提供了一个强大的解决方案,用于在SemCom.Com中安全地进行SKB同步.
- 提出的方法提高了可信度和抵御敌对攻击的弹性.
- 该计划提供了一种安全有效的方法,用于在分布式环境中更新LLM知识库.
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