Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things

2025-01-03

AI Summary

To address communication noise, device heterogeneity, dynamic learning objectives, and privacy requirements inherent in multi-source heterogeneous sensor sequences in industrial IoT, this paper proposes the Denoising Adaptive Online Vertical Federated Learning (DAO-VFL) framework. DAO-VFL innovatively co-optimizes local iteration counts and noise robustness by integrating online optimization, adaptive iteration control, and sensor signal denoising. It further provides a theoretically grounded dynamic regret bound analysis. Experiments on two real-world industrial datasets demonstrate that DAO-VFL reduces communication overhead by 32% and improves model accuracy by 5.8% over baseline methods, while simultaneously enhancing robustness against noise and accelerating convergence.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Privacy Preservation
• Adaptive Learning
• Sensor Data Noise

Innovation

• DAO-VFL algorithm
• privacy-preserving learning
• adaptive distributed learning