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Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

arXiv 2605.23984 ↗First posted 15 May 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 43 references that are in the map. First posted on arXiv 15 May 2026.

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29 of 43 references.

References · 29

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17 more
  1. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  2. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  3. 2024Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologiescited by 47
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  6. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  7. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
  8. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  9. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  10. 2022Tighter Regret Analysis and Optimization of Online Federated Learningcited by 14
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  16. 2025Multimodal Online Federated Learning with Modality Missing in Internet of Thingscited by 2
  17. 2025Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learningcited by 2

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