Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments
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Computer Science > Machine Learning
Title:Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments
Abstract:The dynamic nature of Internet of Things (IoT) environments affects the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. Existing adaptive composition methods are mainly based on service replacement or re-composition, where identifying suitable substitutes is difficult and time-consuming. To address this, we propose a novel Test-Time Adaptive (TTA) composition framework for MLaaS in IoT environments. First, we introduce a TTA-aware composability model to determine whether adapted services remain compatible with the existing composition. Next, we design a service-level adaptation model to adjust individual services during inference while preserving composition performance. Experimental results demonstrate that the proposed framework reduces computational time more effectively than traditional adaptive approaches.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.07685 [cs.LG] |
| (or arXiv:2606.07685v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07685
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Sai Krishna Deepak Kanneganti [view email][v1] Fri, 5 Jun 2026 02:52:53 UTC (1,031 KB)
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