mlsanityData to 14 Oct 2026

Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making

arXiv 2502.05220 ↗First posted 5 Feb 2025cs.CR cs.AIcited by 5

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Abstract (arXiv)

Increased utilization of unmanned aerial vehicles (UAVs) in critical operations necessitates secure and reliable communication with Ground Control Stations (GCS). This paper introduces Aero-LLM, a framework integrating multiple Large Language Models (LLMs) to enhance UAV mission security and operational efficiency. Unlike conventional singular LLMs, Aero-LLM leverages multiple specialized LLMs for various tasks, such as inferencing, anomaly detection, and forecasting, deployed across onboard systems, edge, and cloud servers. This dynamic, distributed architecture reduces performance bottleneck and increases security capabilities. Aero-LLM's evaluation demonstrates outstanding task-specific metrics and robust defense against cyber threats, significantly enhancing UAV decision-making and operational capabilities and security resilience against cyber attacks, setting a new standard for secure, intelligent UAV operations.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

12 of 22 references.

References · 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
  2. 2025Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoningcited by 7
  3. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
  4. 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorialcited by 4
  5. 2025AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoringcited by 1