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

Zhaohui Yang, Wei Xu, Le Liang, Yuanhao Cui, Zhijin Qin, Merouane Debbah

TL;DR

This paper surveys privacy, security, and trustworthiness in distributed wireless large AI models (WLAM) within 6G contexts, identifying data‑ and model‑distribution paradigms (e.g., federated vs split learning) and the respective privacy risks. It articulates a multi‑layer defense framework, combining privacy techniques (differential privacy, secure federation, and data obfuscation) with security measures (encryption, cross‑layer aggregation, adversarial training, edge collaboration, and blockchain‑based trust) and ethical governance (semantic communication, explainability, and fairness). The work also outlines future directions, including intelligent adaptive security, explainable security analyses, and blockchain‑based trust management, underscoring the role of EM signal processing in enabling robust, private, and trustworthy WLAM deployments. Overall, the paper provides a structured synthesis of methods and research directions to realize private, secure, and trustworthy distributed WLAM systems for real‑time wireless AI applications in 6G and beyond.

Abstract

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.

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

TL;DR

This paper surveys privacy, security, and trustworthiness in distributed wireless large AI models (WLAM) within 6G contexts, identifying data‑ and model‑distribution paradigms (e.g., federated vs split learning) and the respective privacy risks. It articulates a multi‑layer defense framework, combining privacy techniques (differential privacy, secure federation, and data obfuscation) with security measures (encryption, cross‑layer aggregation, adversarial training, edge collaboration, and blockchain‑based trust) and ethical governance (semantic communication, explainability, and fairness). The work also outlines future directions, including intelligent adaptive security, explainable security analyses, and blockchain‑based trust management, underscoring the role of EM signal processing in enabling robust, private, and trustworthy WLAM deployments. Overall, the paper provides a structured synthesis of methods and research directions to realize private, secure, and trustworthy distributed WLAM systems for real‑time wireless AI applications in 6G and beyond.

Abstract

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.

Paper Structure

This paper contains 38 sections, 2 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Motivation
  3. Classifications of WLAM
  4. Contributions and Structure
  5. Privacy and Security for Distributed WLAM
  6. Privacy Analysis
  7. Transmission of Raw Data
  8. Transmission of Accurate Model Parameters
  9. Transmission of Inaccurate Model Parameters
  10. Security Analysis
  11. Attack Types and Challenges for WLAM
  12. Data Poisoning Attacks
  13. Model Injection Attacks and Environmental Complexity
  14. Dynamic Byzantine Attacks and Adaptive Strategies
  15. Channel Manipulation Adversarial Attacks
  16. ...and 23 more sections

Figures (4)

  • Figure 1: Two potential classifications of distributed WLAM.
  • Figure 2: The structure of this paper.
  • Figure 3: Illustration of model distributed semantic communication system.
  • Figure 4: Application of EM signal processing in WLAM.