Distributed Anomaly Detection With Attention-Guided Diffusion Models and Client-Side Defect Generation
Bibliographic record
Abstract
Modern industrial systems are increasingly defined by geographically distributed production lines, stringent privacy constraints, particularly to protect intellectual property and manufacturing process details, and heterogeneous data pipelines. In such environments, centralized anomaly detection (AD) is often impractical due to data governance restrictions and limited computational resources at local sites. To address these challenges, we propose a modular and lightweight AD framework based on diffusion models, named D-ADDA (distributed anomaly detection based on data augmentation), designed for distributed deployment. Unlike many state-of-the-art methods that depend on large pretrained models or external datasets, our approach is trained entirely on defective data locally available, enhancing privacy and domain specificity. A novel data augmentation module generates diverse defective samples through a multistage pipeline, which are used to train an attention-based diffusion model for defect synthesis. This architecture supports dislocated components across multiple clients, enabling training and inference in resource-constrained or privacy-sensitive settings. Experimental results on the MVTec AD dataset confirm the effectiveness of our approach, achieving an average classification accuracy of 60.46% across 14 categories, outperforming state-of-the-art approaches, with competitive localization performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".