GNSS Interference Classification Using Federated Reservoir Computing

TL;DR

This paper presents Federated Reservoir Computing (FedRC), a novel, efficient approach for classifying GNSS interference in UAVs that outperforms traditional methods in convergence speed and loss reduction.

Contribution

The paper introduces FedRC, a new federated learning framework using reservoir computing to improve GNSS interference classification for UAVs, reducing computational load and data management issues.

Findings

FedRC achieves faster convergence than traditional models.

FedRC maintains lower loss levels during training.

FedRC demonstrates high adaptability and efficiency in GNSS interference classification.

Abstract

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite Systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency.