用专家混合模型提升6G语义通信安全性，能同时抵御多种攻击。
Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks
- 采用门控网络动态选择专攻不同安全问题的专家
- 可同时应对多种异构攻击，对任务准确率影响极小
- 适合车联网等高安全需求场景
语义通信（SemCom）在6G网络中扮演关键角色，为未来高效通信提供可行方案。基于深度学习的语义编解码器进一步提升了效率。然而，深度学习模型易受对抗攻击等安全威胁，导致消息篡改和隐私泄露，尤其在无线通信场景下风险更高。现有防御方法常无法同时应对多种异构攻击。为此，本文提出一种新型基于专家混合（MoE）的语义通信系统。该系统包含一个门控网络和多个专家，每个专家专精于不同安全挑战。门控网络根据用户定义的安全需求，自适应选择合适专家以应对异构攻击。多个专家协同完成语义通信任务，满足用户安全要求。在车联网场景的案例研究中，仿真结果表明，所提出的MoE-based SemCom系统能有效缓解并发异构攻击，对下游任务准确率影响最小。
原文摘要 · Abstract (English)
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.
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