探讨无线大模型的隐私安全与可信性，助力6G智能应用落地
On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
- 系统分析分布式无线大模型的隐私与安全机制
- 提出无线环境下大模型部署的可信性评估框架
- 适合关注6G智能系统安全的科研与工程人员
将无线通信与大型人工智能模型结合，可开启自动驾驶、智慧城市和物联网等实时应用新场景。在第六代移动通信（6G）网络中，无处不在的通信与计算资源使大型AI模型能够提供普惠服务。然而，安全考量与可持续通信资源限制了其在分布式无线网络中的部署。本文全面综述了分布式无线大模型（WLAM）的隐私、安全与可信性问题，首次详细分析了分布式WLAM的隐私与安全特性，讨论了其分类体系与理论发现，并阐述了实施过程中的可信性与伦理问题。最后，在电磁信号处理背景下展示了分布式WLAM的综合应用场景。
原文摘要 · Abstract (English)
Combining wireless communication with large artificial intelligence (AI) models can open up a myriad of novel application scenarios. In sixth generation (6G) networks, ubiquitous communication and computing resources allow large AI models to serve democratic large AI models-related services to enable real-time applications like autonomous vehicles, smart cities, and Internet of Things (IoT) ecosystems. However, the security considerations and sustainable communication resources limit the deployment of large AI models over distributed wireless networks. This paper provides a comprehensive overview of privacy, security, and trustworthy for distributed wireless large AI model (WLAM). In particular, a detailed privacy and security are analysis for distributed WLAM is fist revealed. The classifications and theoretical findings about privacy and security in distributed WLAM are discussed. Then the trustworthy and ethics for implementing distributed WLAM are described. Finally, the comprehensive applications of distributed WLAM are presented in the context of electromagnetic signal processing.
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