用大模型增强联邦学习，提升边缘云AI安全与隐私保护
Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models
- 结合大模型与安全多方计算，优化分布式数据聚合加密
- 实验显示数据保护和抗攻击能力比传统方法高15%
- 适合关注边缘计算安全与隐私保护的研究者
随着边缘计算与云系统在人工智能应用中的广泛应用，如何在保障数据隐私的同时维持高效性能已成为紧迫的安全问题。本文提出一种基于联邦学习的数据协同方法，利用大规模语言模型（LLMs）增强边缘云AI系统的数据隐私保护与系统鲁棒性。在现有联邦学习框架基础上，引入安全多方计算协议，通过大模型优化分布式节点间的数据聚合与加密过程，确保数据隐私并提升系统效率。结合先进的对抗训练技术，模型显著增强了对数据泄露与模型投毒等安全威胁的抵抗能力。实验结果表明，该方法在数据保护与模型鲁棒性方面比传统联邦学习方法提升15%。
原文摘要 · Abstract (English)
With the widespread application of edge computing and cloud systems in AI-driven applications, how to maintain efficient performance while ensuring data privacy has become an urgent security issue. This paper proposes a federated learning-based data collaboration method to improve the security of edge cloud AI systems, and use large-scale language models (LLMs) to enhance data privacy protection and system robustness. Based on the existing federated learning framework, this method introduces a secure multi-party computation protocol, which optimizes the data aggregation and encryption process between distributed nodes by using LLM to ensure data privacy and improve system efficiency. By combining advanced adversarial training techniques, the model enhances the resistance of edge cloud AI systems to security threats such as data leakage and model poisoning. Experimental results show that the proposed method is 15% better than the traditional federated learning method in terms of data protection and model robustness.
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