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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

Jiayi He, Xiaofeng Luo, Jiawen Kang, Hongyang Du, Zehui Xiong, Ci Chen, Dusit Niyato, Xuemin Shen

TL;DR

A novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges, that effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy is proposed.

Abstract

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy.

Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

TL;DR

A novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges, that effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy is proposed.

Abstract

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy.
Paper Structure (18 sections, 3 figures, 1 table)

This paper contains 18 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Semantic Communication in 6G Network
  3. Review of Semantic Communication
  4. Semantic Communication Applications in 6G Networks
  5. Security Issues and Defense Schemes of SemCom
  6. Mixture-of-Experts (MoE)-based Semantic Communication for Trustworthy 6G Networks
  7. Mixture-of-Experts (MoE) Network Model
  8. An MoE-based Semantic Communication System for Trustworthy 6G Networks
  9. Gating Network and Experts Architectures and Training Method
  10. Case Study
  11. System Model
  12. Simulation Settings
  13. Simulation Results
  14. Future Directions
  15. Input Simplification for MoE-based SemCom
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overview of semantic communication applications in space-air-ground-sea integrated 6G networks.
  • Figure 2: A general Mixture-of-Experts (MoE)-based Semantic Communication (SemCom) system building upon user-defined security requirements toward trustworthy 6G networks.
  • Figure 3: The performance of proposed MoE-based SemCom system facing single and multiple heterogeneous attacks.