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<p>The increasing use of the Unmanned Aerial Vehicle (UAV) swarms in real-time and mission-critical operations requires such communication infrastructure not only to meet security and adaptation demands, but also to be transparent, interpretable. This article gives an Explainable Multi-Agent Reinforcement Learning (EMARL) framework of an intelligent and safe Flying Ad Hoc Networks (FANETs) communication model. The offered system combines a decentralized learning system by Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, a trust-based security system, and an Explainable AI (XAI) package of SHAP, LIME, and attention visualization techniques. The EMARL system allows every UAV agent to autonomously base their decision on the routing policy that is both interpretable and the result of a combination of local observations, learned policies, as well as trust estimates of the adjacent agents. Network modeling and mobility dynamics of UAV is simulated through NS-3, UAV mobility dynamics through AirSim, and a Python-based MARL engine in order to train policies and make decisions that are coordinated. The evaluation of the performance reveals that EMARL has always seen improved packet delivery ratio (PDR), improved accuracy, reduced delay, improved energy efficiency and false positive rate over the traditional protocols like, Ad hoc On-Demand Distance Vector Routing (AODV), Trust based, Q-Routing and Standard, MARL even under jamming and Sybil attack conditions. Exploitability-based measurements also validate the framework as an entity that provides clarity and accountability of decisions, thus enhancing human interpretability and credence. At the ablation studies, the presence of the XAI and trust modules is deemed to be essential to ensure the robustness of the system. Comprehensively, the EMARL framework is the vital step on the path aiming at safe, interpretable, and scalable UAV swarm communications in dynamic and hostile scenes.</p>

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Explainable multi agent reinforcement learning framework for secure and adaptive communication in UAV swarm based fanets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Hend Khalid Alkahtani,
  • Ybytayeva Galiya,
  • Bekarystankyzy Akbayan,
  • Ayman Qahmash

摘要

The increasing use of the Unmanned Aerial Vehicle (UAV) swarms in real-time and mission-critical operations requires such communication infrastructure not only to meet security and adaptation demands, but also to be transparent, interpretable. This article gives an Explainable Multi-Agent Reinforcement Learning (EMARL) framework of an intelligent and safe Flying Ad Hoc Networks (FANETs) communication model. The offered system combines a decentralized learning system by Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, a trust-based security system, and an Explainable AI (XAI) package of SHAP, LIME, and attention visualization techniques. The EMARL system allows every UAV agent to autonomously base their decision on the routing policy that is both interpretable and the result of a combination of local observations, learned policies, as well as trust estimates of the adjacent agents. Network modeling and mobility dynamics of UAV is simulated through NS-3, UAV mobility dynamics through AirSim, and a Python-based MARL engine in order to train policies and make decisions that are coordinated. The evaluation of the performance reveals that EMARL has always seen improved packet delivery ratio (PDR), improved accuracy, reduced delay, improved energy efficiency and false positive rate over the traditional protocols like, Ad hoc On-Demand Distance Vector Routing (AODV), Trust based, Q-Routing and Standard, MARL even under jamming and Sybil attack conditions. Exploitability-based measurements also validate the framework as an entity that provides clarity and accountability of decisions, thus enhancing human interpretability and credence. At the ablation studies, the presence of the XAI and trust modules is deemed to be essential to ensure the robustness of the system. Comprehensively, the EMARL framework is the vital step on the path aiming at safe, interpretable, and scalable UAV swarm communications in dynamic and hostile scenes.