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.
By Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan
arXiv:2610.14815v1 Announce Type: cross
Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architec...
By Prince Kwabena Appiah Boadu, Jeffery Baafi Kyei, Enoch Kofi Koranteng, Griffth Selorm Klogo
The paper introduces AIGS, a lightweight Adaptive Incremental Gating System designed for online representation learning in non‑stationary data streams. AIGS uses a Shock Ratio feedback signal to drive a Continuous Plasticity Controller, enabling smooth adaptation between learning plasticity and memory retention while keeping per‑step complexity linear in the feature dimension. Experiments on smart‑city traffic, meteorological, and industrial datasets show that AIGS improves early‑warning lead times, accelerates recovery after abrupt changes, and enhances anomaly recall in noisy environments.
By SiRui He, Kai Liang Lew, Chui Zi Ong, Chean Khim Toa
arXiv:2606. 14987v1 Announce Type: cross Abstract: Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility.
By Oxana Salish, Kuniyilh S
CoMemNet is a Continual Memory Network designed for traffic prediction on evolving sensor networks. It combines an online branch that adapts to current data with an exponential‑moving‑average target branch for stable reference, and uses a Wasserstein‑based drift sampler to selectively update drift‑sensitive nodes. A lightweight temporal memory replay buffer stores compact states, allowing efficient adaptation without traversing all historical data. Experiments on PeMS datasets show that CoMemNet maintains stable accuracy and outperforms retraining baselines in both prediction performance and training time.
By Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).
By Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh
arXiv:2604. 06435v2 Announce Type: replace-cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare.
By Manuel Barusco, Francesco Borsatti, David Petrovic, Davide Dalle Pezze, Gian Antonio Susto
arXiv:2606. 11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity.
By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni
arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
By Md Moman Ul Haque Khan, Samira Sadaoui
arXiv:2406.02447v5 Announce Type: replace
Abstract: Federated Learning (FL) aims at unburdening the training of deep models by distributing computation across multiple devices (clients) while safegua...
By Riccardo Salami, Pietro Buzzega, Matteo Mosconi, Mattia Verasani, Simone Calderara
arXiv:2607. 19661v1 Announce Type: cross Abstract: Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learning from new data in the field.
By Nabila Tasnim, Haoran Liu, Qing Cao, Saugata Ghose
The paper presents TASTE, a method that uses Bayesian optimization to tune batch size for on‑device edge learning, aiming to maximize hardware throughput while preserving accuracy. Experiments on devices like the Raspberry Pi 4 show that the tuned batch size, combined with gradient accumulation and linear learning‑rate scaling, can double training throughput compared to using the maximum batch size. In online continual learning, the optimal batch size also helps balance stability and plasticity, reducing catastrophic forgetting without sacrificing efficiency.
By Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann