面向工业物联网中序列多传感器数据的去噪与自适应在线纵向联邦学习
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things
AI总结：
针对工业装配线多传感器序列数据的隐私、通信噪声与异构性问题，提出DAO-VFL在线纵向联邦学习算法，并通过遗憾界分析和两个真实数据集验证其优越性能。
摘要：
随着工业物联网中智能传感器等边缘设备计算能力的不断提升，这些传感器不再局限于单纯的数据采集，而是越来越能够执行复杂的计算任务。这一进展为采用分布式学习方法提供了动机和基础。本研究聚焦于工业装配线场景，其中分布在不同位置的多个传感器按顺序采集具有不同特征空间的实时数据。为利用这些传感器的计算潜力，同时解决集中式学习固有的通信开销和隐私问题，我们提出了去噪与自适应在线纵向联邦学习（Denoising and Adaptive Online Vertical Federated Learning，DAO-VFL）算法。DAO-VFL针对工业装配线场景定制，可有效管理连续数据流并适应不断变化的学习目标。此外，它能够应对工业环境中普遍存在的关键挑战，例如通信噪声和传感器能力的异构性。为支持所提出的算法，我们提供了全面的理论分析，重点阐明去噪和自适应本地迭代决策对遗憾界的影响。在两个真实世界数据集上的实验结果进一步证明，与基准算法相比，DAO-VFL具有优越的性能。
英文摘要：
With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motivation and the foundation for adopting distributed learning approaches. This study focuses on an industrial assembly line scenario where multiple sensors, distributed across various locations, sequentially collect real-time data characterized by distinct feature spaces. To leverage the computational potential of these sensors while addressing the challenges of communication overhead and privacy concerns inherent in centralized learning, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm. Tailored to the industrial assembly line scenario, 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 algorithms.
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