  The increasing demand for wireless communication underscores the need to
optimize radio frequency spectrum utilization. An effective strategy for
leveraging underutilized licensed frequency bands is cooperative spectrum
sensing (CSS), which enable multiple secondary users (SUs) to collaboratively
detect the spectrum usage of primary users (PUs) prior to accessing the
licensed spectrum. The increasing popularity of machine learning has led to a
shift from traditional CSS methods to those based on deep learning. However,
deep learning-based CSS methods often rely on centralized learning, posing
challenges like communication overhead and data privacy risks. Recent research
suggests vertical federated learning (VFL) as a potential solution, with its
core concept centered on partitioning the deep neural network into distinct
segments, with each segment is trained separately. However, existing VFL-based
CSS works do not fully address the practical challenges arising from streaming
data and the objective shift. In this work, we introduce online vertical
federated learning (OVFL), a robust framework designed to address the
challenges of ongoing data stream and shifting learning goals. Our theoretical
analysis reveals that OVFL achieves a sublinear regret bound, thereby
evidencing its efficiency. Empirical results from our experiments show that
OVFL outperforms benchmarks in CSS tasks. We also explore the impact of various
parameters on the learning performance.
