Distributed Data Grading with Privacy-Enhanced in Internet of Unmanned Agent: A Federated Hybrid Deep Learning Approach

The Internet of unmanned agents (IUAs) has emerged as a transformative technology, driven by advancements in unmanned devices and 5G communications, leading to significant progress in autonomous systems and distributed networks. Despite these advancements, IUA faces significant challenges in data security, privacy preservation, and distributed learning, particularly in grading sensitive data and efficient distributed grade model training across diverse unmanned devices within IUA context. To address these issues, this article proposes a novel scheme, FedHDL-IUA, a privacy-enhanced distributed data grading scheme designed specifically for the IUA context. This scheme leverages a federated learning (FL) framework, ensuring the privacy of local sensitive data while optimizing the performance of distributed data grading models. By combining bidirectional long short-term memory (BiLSTM) and residual networks (ResNets), the scheme can effectively capture feature dependencies in diverse network traffic data, thereby enhancing the accuracy and efficiency of the data grading model. The simulation experiments are conducted using two open-source datasets and a private dataset, and the results show that FedHDL-IUA can efficiently and effectively grade the traffic data in both centralized and FL modes and outperforms other existing schemes and traditional deep learning models in terms of performance.