Copertina

K-Means Based TinyML Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Albaiz, Abdulrahman, Amsaad, Fathi
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
di: Albaiz, Abdulrahman, et al.
Pubblicazione: (2026)

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
di: Almaini, Amar, et al.
Pubblicazione: (2026)

Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
di: Dehrouyeh, Fatemeh, et al.
Pubblicazione: (2025)

Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection
di: Ping, Jared M., et al.
Pubblicazione: (2024)

TinySV: Speaker Verification in TinyML with On-device Learning
di: Pavan, Massimo, et al.
Pubblicazione: (2024)

Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification
di: Ghajari, Ghazal, et al.
Pubblicazione: (2024)

Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD
di: Ghajari, Ghazal, et al.
Pubblicazione: (2025)

MLonMCU: TinyML Benchmarking with Fast Retargeting
di: van Kempen, Philipp, et al.
Pubblicazione: (2023)

Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data
di: Baligodugula, Vishnu Vardhan, et al.
Pubblicazione: (2025)

TinyML-Enabled IoT for Sustainable Precision Irrigation
di: Taueatsoala, Kamogelo, et al.
Pubblicazione: (2026)

Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
di: Armeniakos, Giorgos, et al.
Pubblicazione: (2024)

MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
di: Carnelos, Matteo, et al.
Pubblicazione: (2024)

Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
di: Deutel, Mark, et al.
Pubblicazione: (2023)

Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
di: Shalby, Hazem Hesham Yousef, et al.
Pubblicazione: (2025)

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
di: Zeinaty, Christophe El, et al.
Pubblicazione: (2025)

Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML
di: Shaalan, Yassien
Pubblicazione: (2026)

On-device Online Learning and Semantic Management of TinyML Systems
di: Ren, Haoyu, et al.
Pubblicazione: (2024)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
di: Huckelberry, Jacob, et al.
Pubblicazione: (2024)

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
di: Han, Lixiang, et al.
Pubblicazione: (2024)

On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
di: Puslecki, Tobiasz, et al.
Pubblicazione: (2025)

Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
di: Wu, Guanghan, et al.
Pubblicazione: (2025)

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
di: Leslin, Jelin, et al.
Pubblicazione: (2025)

What changes after deployment? A survey on On-device Learning in TinyML
di: Pavan, Massimo, et al.
Pubblicazione: (2026)

Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI
di: Bartoli, Pietro, et al.
Pubblicazione: (2025)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
di: Dehrouyeh, Fatemeh, et al.
Pubblicazione: (2024)

Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
di: Bao, Ziyuan, et al.
Pubblicazione: (2025)

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
di: Suwannaphong, Thanaphon, et al.
Pubblicazione: (2024)

Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models
di: Vahdatpour, Mohammad Saleh, et al.
Pubblicazione: (2026)

U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
di: Huang, Zhaolan, et al.
Pubblicazione: (2023)

TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification
di: Saha, Bidyut, et al.
Pubblicazione: (2024)

Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers
di: Samanta, Riya, et al.
Pubblicazione: (2024)

Integration of TinyML and LargeML: A Survey of 6G and Beyond
di: Vu, Thai-Hoc, et al.
Pubblicazione: (2025)

Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset
di: Ghajari, Ghazal, et al.
Pubblicazione: (2025)

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption
di: Rüb, Marcus, et al.
Pubblicazione: (2024)

msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
di: Huang, Zhaolan, et al.
Pubblicazione: (2025)

A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
di: Sucipto, Willy, et al.
Pubblicazione: (2025)

TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
di: Huang, Zhaolan, et al.
Pubblicazione: (2024)

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
di: Samakovlis, Dimitrios, et al.
Pubblicazione: (2024)

SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML
di: Lamaakal, Ismail, et al.
Pubblicazione: (2025)

Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
di: Wiese, Philip, et al.
Pubblicazione: (2024)