Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
This paper proposes a novel framework called MODIAD for multimodal online distributed industrial anomaly detection. It formulates a Multi-class Intelligent Scheduling (MIS) problem and designs a Sequential Marginal Gain Greedy (SMG) algorithm to solve it efficiently. Furthermore, a Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy reduces training overhead. Experiments on MVTec 3D-AD and Eyecandies datasets demonstrate superior performance and efficiency.
Article intelligence
Key points
- Existing industrial anomaly detection methods are mostly centralized and offline, ignoring distributed and streaming data.
- The MODIAD framework integrates multi-class scheduling and edge intelligence for online distributed training.
- SMG algorithm and REC-LoRA strategy address scheduling and efficiency challenges respectively.
Why it matters
This matters because existing industrial anomaly detection methods are mostly centralized and offline, ignoring distributed and streaming data.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23984] Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
[Submitted on 15 May 2026]
Title:Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
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Abstract: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.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.23984 [cs.LG]
(or arXiv:2605.23984v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.23984
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Heqiang Wang [view email] [v1] Fri, 15 May 2026 08:25:40 UTC (318 KB)
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