On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
It addresses security and resource limitations for deploying AI models in 6G networks, which is incremental as it reviews existing concerns rather than proposing new solutions.
This paper tackles the challenges of deploying large AI models in distributed wireless networks by providing a comprehensive overview of privacy, security, and trustworthiness issues, including classifications and theoretical findings, with applications in electromagnetic signal processing.
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.