mlsanityData to 7 Oct 2026

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

arXiv 2606.07685 ↗First posted 5 Jun 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 5 Jun 2026.

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References · 15

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  3. 2020Improving robustness against common corruptions by covariate shift adaptationcited by 427
  4. 2021MEMO: Test Time Robustness via Adaptation and Augmentationcited by 376
  5. 2019MNIST-C: A Robustness Benchmark for Computer Visioncited by 137
  6. 2023TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptationcited by 101
  7. 2020FLaaS: Federated Learning as a Servicecited by 22
  8. 2023Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptationcited by 14
  9. 2024A Layer Selection Approach to Test Time Adaptationcited by 7
  10. 2025Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environmentscited by 3
  11. 2021Long-term IaaS Provider Selection using Short-term Trial Experiencecited by 2
  12. 2025Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computingcited by 2
3 more
  1. 2026Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environmentscited by 2
  2. 2015Semantic Service Substitution in Pervasive Environmentscited by 1
  3. 2025pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Datacited by 1

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