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

arXiv: https://arxiv.org/abs/2606.07685 · HTML: https://mlsanity.com/paper/2606.07685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Improving robustness against common corruptions by covariate shift adaptation](https://mlsanity.com/paper/2006.16971/) (2020)
- [MEMO: Test Time Robustness via Adaptation and Augmentation](https://mlsanity.com/paper/2110.09506/) (2021)
- [MNIST-C: A Robustness Benchmark for Computer Vision](https://mlsanity.com/paper/1906.02337/) (2019)
- [TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation](https://mlsanity.com/paper/2302.05155/) (2023)
- [FLaaS: Federated Learning as a Service](https://mlsanity.com/paper/2011.09359/) (2020)
- [Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation](https://mlsanity.com/paper/2311.05858/) (2023)
- [A Layer Selection Approach to Test Time Adaptation](https://mlsanity.com/paper/2404.03784/) (2024)
- [Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2506.11054/) (2025)
- [Long-term IaaS Provider Selection using Short-term Trial Experience](https://mlsanity.com/paper/2102.12222/) (2021)
- [Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing](https://mlsanity.com/paper/2501.15203/) (2025)
- [Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments](https://mlsanity.com/paper/2601.12305/) (2026)
- [Semantic Service Substitution in Pervasive Environments](https://mlsanity.com/paper/1505.04527/) (2015)
- [pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data](https://mlsanity.com/paper/2511.18066/) (2025)

## 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.