# On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)

arXiv: https://arxiv.org/abs/2412.02538 · HTML: https://mlsanity.com/paper/2412.02538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [A Joint Communication and Computation Design for Distributed RISs Assisted Probabilistic Semantic Communication in IIoT](https://mlsanity.com/paper/2404.19750/) (2024)
- [Integrating Pre-Trained Language Model with Physical Layer Communications](https://mlsanity.com/paper/2402.11656/) (2024)
- [Multi-Modal Fusion-Based Multi-Task Semantic Communication System](https://mlsanity.com/paper/2407.00964/) (2024)

## Built on by (most cited first, 1 of 1)

- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)

## Abstract (arXiv)

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.