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.
翻译:近些年来,无线数据传输需求的指数式增长增加了对准确频谱遥感方法提高频谱效率的迫切性。传统频谱遥感方法不可靠,使用单一二级用户(SU)的测量促进了对合作频谱遥感的研究。在这项工作中,我们提议了一个纵向联合学习框架,以利用多个SU的分布特征,同时不损害数据隐私。然而,VFL的重复培训过程面临着高通信延迟度问题。为加快培训进程,我们提议了一条短线垂直联合学习算法(T-VFLL),通过将标准VFLL算法与频道观测用户的排期政策相结合,使培训延时率大大降低。T-VFLL的趋同性能是通过数学分析提供的,并通过模拟结果来证明。此外,为了保证T-VFL算法的趋同性,我们完成了关于VFL框架下使用的神经结构的三项设计规则,其有效性通过模拟得到证明。