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

Hend Khalid Alkahtani1, Ybytayeva Galiya2, Bekarystankyzy Akbayan3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia.

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Summary

This study introduces an Explainable Multi-Agent Reinforcement Learning (EMARL) framework for secure and interpretable Unmanned Aerial Vehicle (UAV) swarm communications. The EMARL system enhances packet delivery, accuracy, and efficiency while reducing delay and false positives, even under attacks.

Keywords:
Explainable AIFANET securityMulti-Agent reinforcement learningTrust-Based routingUAV swarm communication

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Security

Background:

  • Unmanned Aerial Vehicle (UAV) swarms require secure, transparent, and interpretable communication for critical operations.
  • Existing communication protocols struggle to meet the demands of dynamic and potentially hostile UAV swarm environments.
  • The need for explainable AI (XAI) in autonomous systems is growing for trust and accountability.

Purpose of the Study:

  • To propose an Explainable Multi-Agent Reinforcement Learning (EMARL) framework for intelligent and safe Flying Ad Hoc Networks (FANETs).
  • To enhance the security, interpretability, and performance of UAV swarm communications.
  • To ensure autonomous decision-making based on local observations, learned policies, and trust estimates.

Main Methods:

  • Developed an EMARL system integrating Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for decentralized learning.
  • Incorporated a trust-based security system and Explainable AI (XAI) techniques (SHAP, LIME, attention visualization).
  • Simulated network and UAV mobility using NS-3 and AirSim, with a Python-based MARL engine for policy training.

Main Results:

  • EMARL demonstrated superior performance over traditional protocols (AODV, Q-Routing) in packet delivery ratio (PDR), accuracy, and energy efficiency.
  • The framework significantly reduced delay and false positive rates, proving robust against jamming and Sybil attacks.
  • Ablation studies confirmed the essential role of XAI and trust modules in system robustness and decision accountability.

Conclusions:

  • The EMARL framework provides a vital advancement for safe, interpretable, and scalable UAV swarm communications.
  • The system enhances human interpretability and credence through clear and accountable decision-making processes.
  • EMARL offers a robust solution for UAV swarm communication in dynamic and hostile operational environments.