The ever-rising applications of drones in the Internet of Things (IoT) era is offering many opportunities and challenges. Owing to drone abilities (silent flying, capturing photos and videos, etc.), there is widespread concern about drone authentication and which drones allow to fly. In this regard, there are several machine learning (ML) proposals for authentication in IoT networks. Such ML-based models have drawbacks in data security, privacy-preserving, and scalability when applied in drone authentication. ML-based methods collect all data and centrally train the authentication model, exposing the model to adversarial situations. This paper proposes a federated learning-based drone authentication model with drones’ Radio Frequency (RF) features in IoT networks. In the proposed model, the Deep Neural Network (DNN) architecture is implemented for drone authentication with Stochastic Gradient Descent (SGD) optimization performed locally on drones. Also, Homomorphic encryption and the secure aggregation method are applied to secure model parameters. Experimental results show that the federated drone authentication model gains a high true positive rate (TPR) during drone authentication and better performance compared to other ML-based models.

无人机在物联网（IoT）时代不断增长的应用带来了许多机遇和挑战。由于无人机的能力（静音飞行、拍摄照片和视频等），人们普遍关注无人机身份验证以及哪些无人机允许飞行。在这方面，有几个机器学习 (ML) 建议用于物联网网络中的身份验证。当应用于无人机身份验证时，这种基于 ML 的模型在数据安全性、隐私保护和可扩展性方面存在缺陷。基于机器学习的方法收集所有数据并集中训练身份验证模型，将模型暴露于对抗性情况。本文提出了一种基于联合学习的无人机身份验证模型，该模型具有物联网网络中无人机的射频 (RF) 特性。在提出的模型中，深度神经网络 (DNN) 架构通过在无人机上本地执行的随机梯度下降 (SGD) 优化实现无人机身份验证。此外，将同态加密和安全聚合方法应用于安全模型参数。实验结果表明，与其他基于机器学习的模型相比，联合无人机认证模型在无人机认证过程中获得了较高的真阳性率（TPR）和更好的性能。