[Submitted on 29 Oct 2026]
Title:Don't Just Impute, Detect: Task-Aware Diffusion Pipelines for Edge Anomaly Detection
View a PDF of the paper titled Don't Just Impute, Detect: Task-Aware Diffusion Pipelines for Edge Anomaly Detection, by Jeongsu Park and 4 other authors
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Abstract:Industrial Internet of Things (IIoT) edge pipelines routinely lose contiguous blocks of multi-sensor data to network dropouts and hardware faults. The standard remedy is a disjointed "impute-then-detect" pipeline whose imputer is trained only for Mean Squared Error (MSE). Such an imputer fills a missing block with the most probable normal pattern: a partially observed anomaly is smoothed back toward normality, and boundary artifacts on normal data raise false alarms. We call this imputation bias and propose the Task-Aware Diffusion Refiner (TADR), a conditional diffusion refiner trained against the loss landscape of a frozen downstream detector. Because back-propagating through the sampling trajectory is intractable, the task-aware terms act on the one-step clean-window estimate that the noise prediction already yields, so training costs no more than plain diffusion training. Under strict Point-to-Point evaluation over five seeds with paired significance tests, TADR raises F1 by $0.338$ over the unrefined detector on SMD ($p = 0.0015$), and the anomaly enhancement objective alone contributes $0.463$ in ablation. We also characterise when the method helps: the gain tracks the continuous, mutually redundant channels available and the anomaly evidence the outage removes, and it is absent on telemetry datasets dominated by one-hot command channels and on PSM. At ten sampling steps TADR runs in $41.4$ ms per window on a single CPU core, sustaining about $24$ concurrent 1 Hz streams from a $4.03$ MB model. Code and per-seed results are available at this https URL.
Comments: 11 pages, 6 figures. Accepted at the 33rd IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2026)
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2610.40732 [cs.LG]
(or arXiv:2610.40732v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.40732
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Geonwoo Kim [view email] [v1] Thu, 29 Oct 2026 12:23:18 UTC (1,762 KB)
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