arXiv AI August 20, 2026 By Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan

Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments

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The paper introduces a framework for detecting performance drift in Machine Learning as a Service (MLaaS) tailored to Internet of Things (IoT) settings. It first builds an extraction model that learns the service’s behavior from input‑output pairs, then uses this to jointly monitor changes in data and service behavior. An adaptive temporal mechanism adjusts monitoring frequency, and experiments on real datasets show significant accuracy gains and reduced miss‑detection rates compared to baseline methods.

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