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Drones have become a vital part of the technology-driven world we live in today. From using it for delivering our day-to-day items to high grade military purposes, drones have spread their demands to various sectors of the economy. However, a little exploration has been done when it comes to talking about their security. Security generally comprises two aspects: authentication and authorization. While authentication is more to do with confirming the users with who they say they are, authorization is when the user’s permissions to access a resource is examined and a decision to approve or deny the access is taken by the system. This paper discusses authorization in detail using reinforcement learning and federated learning. The model is trained on a synthetic dataset, and is evaluated based on progressive validation loss (PVL), F \(_{1}\) -Score, and a new metric called Permit Score.

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Attribute Based Federated-Reinforcement Learning Approach for Drone Authorization

  • 会议论文

  • 发表日期：

    2025年

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  • K. Rajesh Rao,
  • Tribikram Pradhan,
  • K. Krishna Prakasha

摘要

Drones have become a vital part of the technology-driven world we live in today. From using it for delivering our day-to-day items to high grade military purposes, drones have spread their demands to various sectors of the economy. However, a little exploration has been done when it comes to talking about their security. Security generally comprises two aspects: authentication and authorization. While authentication is more to do with confirming the users with who they say they are, authorization is when the user’s permissions to access a resource is examined and a decision to approve or deny the access is taken by the system. This paper discusses authorization in detail using reinforcement learning and federated learning. The model is trained on a synthetic dataset, and is evaluated based on progressive validation loss (PVL), F \(_{1}\) -Score, and a new metric called Permit Score.