用多个专用大模型分布式部署，提升无人机通信安全与智能决策能力
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making
- 采用多任务专用大模型分层部署于机载、边缘与云端
- 在多种任务中表现优异，有效防御网络攻击
- 适合需高安全性的无人机智能控制系统研发者
在关键任务中日益普及的无人飞行器（UAV）需要与地面控制站（GCS）之间实现安全可靠的通信。本文提出Aero-LLM框架，通过集成多个专用大语言模型（LLMs），提升无人机任务的安全性与运行效率。不同于传统的单一模型架构，Aero-LLM将多个专业化的LLMs分别用于推理、异常检测和预测等任务，并动态部署于机载系统、边缘节点及云服务器。这种分布式架构有效缓解性能瓶颈，增强安全防护能力。评估结果表明，Aero-LLM在各类任务中均表现突出，具备强抗网络攻击能力，显著提升无人机的智能决策水平与系统韧性，为安全、智能的无人机操作树立新标准。
原文摘要 · Abstract (English)
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.
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