Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning

TL;DR

This paper introduces a truncated vertical federated learning framework to enable low-latency cooperative spectrum sensing, improving spectrum efficiency while preserving data privacy across multiple secondary users.

Contribution

The paper proposes a novel T-VFL algorithm combining channel-aware user scheduling with VFL to reduce training latency in spectrum sensing applications.

Findings

T-VFL significantly reduces training latency.

Mathematical analysis confirms convergence performance.

Design rules for neural architectures ensure effective VFL convergence.

Abstract

In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The unreliability of conventional spectrum sensing methods by using measurements from a single secondary user (SU) has motivated research on cooperative spectrum sensing (CSS). In this work, we propose a vertical federated learning (VFL) framework to exploit the distributed features across multiple SUs without compromising data privacy. However, the repetitive training process in VFL faces the issue of high communication latency. To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy. The convergence performance of T-VFL is provided via mathematical analysis and justified by simulation results. Moreover, to guarantee the convergence performance of the T-VFL algorithm, we conclude three design rules on the neural architectures used under the VFL framework, whose effectiveness is proved through simulations.