mlsanityData to 14 Oct 2026

Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments

arXiv 2506.11054 ↗First posted 22 May 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variability of IoT environments lead to fluctuations in data distribution, e.g., concept drift and data heterogeneity, and evolving system requirements, e.g., scalability demands and resource limitations. This paper proposes an adaptive MLaaS composition framework to ensure a seamless, efficient, and scalable MLaaS composition. The framework integrates a service assessment model to identify underperforming MLaaS services and a candidate selection model to filter optimal replacements. An adaptive composition mechanism is developed that incrementally updates MLaaS compositions using a contextual multi-armed bandit optimization strategy. By continuously adapting to evolving IoT constraints, the approach maintains Quality of Service (QoS) while reducing the computational cost associated with recomposition from scratch. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

8 of 32 references.

References · 8

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2019Measure Contribution of Participants in Federated Learningcited by 79
  4. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  5. 2021Composing Energy Services in a Crowdsourced IoT Environmentcited by 3
  6. 2020Long-term IaaS Selection using Performance Discoverycited by 2
  7. 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Servicecited by 2
  8. 2024Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environmentscited by 2
  2. 2026Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environmentscited by 0
  3. 2026Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environmentscited by 0