On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912143076491264 |
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| author | Yang, Zhaohui Xu, Wei Liang, Le Cui, Yuanhao Qin, Zhijin Debbah, Merouane |
| author_facet | Yang, Zhaohui Xu, Wei Liang, Le Cui, Yuanhao Qin, Zhijin Debbah, Merouane |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM) Yang, Zhaohui Xu, Wei Liang, Le Cui, Yuanhao Qin, Zhijin Debbah, Merouane Information Theory Machine Learning 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. |
| title | On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM) |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2412.02538 |