Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in IIoT

With the advancement of computational capabilities in edge devices such as intelligent sensors in the Industrial Internet of Things (IIoT), these sensors evolving beyond simple data collection to support complex computational tasks. This advancement provides new opportunities for adopting distributed learning approaches in IIoT. In this study, we focus on enhancing learning performance in an industrial assembly line scenario where multiple distributed sensors sequentially collect real-time data with distinct feature spaces. However, existing research lacks an online distributed learning framework tailored for such IIoT settings. To address this gap, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm, a novel algorithm that leverages the computing potential of edge sensors while addressing key challenges such as communication overhead and data privacy. DAO-VFL effectively manages continuous data streams and adapts to shifting learning objectives. Furthermore, it can address critical challenges prevalent in industrial environment, such as communication noise and heterogeneity of sensor capabilities. To support the proposed algorithm, we provide a comprehensive theoretical analysis, highlighting the effects of noise reduction and adaptive local iteration decisions on the regret bound. Experimental results on two real-world datasets further demonstrate the superior performance of DAO-VFL compared to benchmarks.