# A Survey on Semantic Communication Networks: Architecture, Security, and Privacy

arXiv: https://arxiv.org/abs/2405.01221 · HTML: https://mlsanity.com/paper/2405.01221/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2024.

## Built on by (most cited first, 22 of 22)

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- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
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- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks](https://mlsanity.com/paper/2409.15695/) (2024)
- [Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images](https://mlsanity.com/paper/2506.19893/) (2025)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence](https://mlsanity.com/paper/2506.10925/) (2025)
- [Trustworthy Semantic Communication for Vehicular Networks: Challenges and Solutions](https://mlsanity.com/paper/2509.20830/) (2025)
- [Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations](https://mlsanity.com/paper/2512.24452/) (2025)
- [Reinforcement-Learned Unequal Error Protection for Quantized Semantic Embeddings](https://mlsanity.com/paper/2601.00186/) (2026)
- [Logic-Driven Semantic Communication for Resilient Multi-Agent Systems](https://mlsanity.com/paper/2601.06733/) (2026)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices](https://mlsanity.com/paper/2602.22794/) (2026)
- [Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks](https://mlsanity.com/paper/2608.03319/) (2026)
- [Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks](https://mlsanity.com/paper/2608.13394/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)
- [Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction](https://mlsanity.com/paper/2609.18368/) (2026)
- [Adaptive Pilot Selection for Unified Semantic Communication and Semantic Sensing in ISAC](https://mlsanity.com/paper/2609.30891/) (2026)

## Abstract (arXiv)

With the rapid advancement and deployment of intelligent agents and artificial general intelligence (AGI), a fundamental challenge for future networks is enabling efficient communications among agents. Unlike traditional human-centric, data-driven communication networks, the primary goal of agent-based communication is to facilitate coordination among agents. Therefore, task comprehension and collaboration become the key objectives of communications, rather than data synchronization. Semantic communication (SemCom) aims to align information and knowledge among agents to expedite task comprehension. While significant research has been conducted on SemCom for two-agent systems, the development of semantic communication networks (SemComNet) for multi-agent systems remains largely unexplored. In this paper, we provide a comprehensive and up-to-date survey of SemComNet, focusing on their fundamentals, security, and privacy aspects. We introduce a novel three-layer architecture for multi-agent interaction, comprising the control layer, semantic transmission layer, and cognitive sensing layer. We explore working modes and enabling technologies, and present a taxonomy of security and privacy threats, along with state-of-the-art defense mechanisms. Finally, we outline future research directions, paving the way toward intelligent, robust, and energy-efficient SemComNet. This survey represents the first comprehensive analysis of SemComNet, offering detailed insights into its core principles as well as associated security and privacy challenges.