Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments

• URL: http://arxiv.org/abs/2606.07685v1
• Date: Fri, 05 Jun 2026 02:52:53 GMT
• Title: Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments
• Abstract summary: We propose a novel Test-Time Adaptive composition framework for Machine Learning in IoT environments.<n>First, we introduce a TTA-aware composability model to determine whether adapted services remain compatible with existing composition.<n>Next, we design a service-level adaptation model to adjust individual services during inference while preserving composition performance.
• Score: 0.06999740786886537
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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