Tiny Machine Learning: Design Principles and Applications

1. Auflage 2025
ISBN: 978-1-394-29454-1
Verlag: Wiley

Buch, Englisch, 784 Seiten

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development

In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design.

Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications.

Additional topics covered in the book include: - A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes
- Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML
- Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis

Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Autoren/Hrsg.

Imoize, Agbotiname Lucky

Do, Dinh-Thuan

Song, Houbing

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie

Inhaltsverzeichnis

About the Editors xxiii
List of Contributors xxvii
Preface xxxv

1 Introduction to TinyML 1
Francisca Onyiyechi Nwokoma, Chidi Ukamaka Betrand, Juliet Nnenna Odii, Euphemia Chioma Nwokorie, and Ikechukwu Ignatius Ayogu

2 Learning Panorama Under TinyML 35
Ikechukwu Ignatius Ayogu, Euphemia Chioma Nwokorie, Juliet Nnenna Odii, Francisca Onyiyechi Nwokoma, and Chidi Ukamaka Betrand

3 TinyML for Anomaly Detection 85
Richard Govada Joshua, Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, and Samuel Oluwatobi Tofade

4 TinyML Power Consumption and Memory in IoT MCUs 163
Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, Richard Govada Joshua, and Samuel Oluwatobi Tofade

5 Efficient Data Cleaning and Anomaly Detection in IoT Devices Using TinyCleanEDF 205
Ilker Kara

6 TinyML Devices and Tools 225
Abeeb Akorede Bello, Agbotiname Lucky Imoize, and Abiodun Temitope Odewale

7 Privacy-Preserving Techniques in TinyML for IoT 259
Oleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Marco Arnesano, and Pavlo Usik

8 Enhancing Cybersecurity in TinyML with Lightweight Cryptographic Algorithms 303
Oleksandr Kuznetsov, Roman Minailenko, Aigul Shaikhanova, Yelyzaveta Kuznetsova, and Agbotiname Lucky Imoize

9 Tiny Machine Learning for Enhanced Edge Intelligence 335
Emmanuel Alozie, Agbotiname Lucky Imoize, Hawau I. Olagunju, Nasir Faruk, Salisu Garba, and Ayobami P. Olatunji

10 Advanced Security Schemes for TinyML Devices 367
Wasswa Shafik and Mumin Adam

11 Robust Ground Truth Data Mining for Enhanced Privacy and Accuracy in Noisy TinyML Environments 403
Yuichi Sei and Agbotiname Lucky Imoize

12 Security and Privacy of TinyML Devices 431
Eftychia Mistillioglou, Evangelia Konstantopoulou, Nicolas Sklavos, and Andronikos Kyriakou

13 Semantic Management of TinyML for Industrial Application 469
Kinzah Noor, Hasnain Ahmad, and Agbotiname Lucky Imoize

14 Fight Poison with Poison: Tiny Machine Learning Resilience Against Poisoning Attacks 503
Tomoki Chiba, Yasuyuki Tahara, Akihiko Ohsuga, Agbotiname Lucky Imoize, and Yuichi Sei

15 TinyML for Real-Time Medical Image Classification and Diagnosis 549
Jelil O. Agbo-Ajala, Lateef A. Akinyemi, Olufisayo S. Ekundayo, and Ernest Mnkandla

16 Biometric Authentication in TinyML: Opportunities and Challenges 587
Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Oleksii Smirnov, and Boris Khruskov

17 Secure Deployment of TinyML Applications: Strategies and Practices 635
Oleksandr Kuznetsov, Sergii Kavun, and Gulvira Bekeshova

18 TinyML for Environmental Applications 665
Duy Nam Khanh Vu and Anh Khoa Dang

19 Benchmarking TinyML Encrypted Federated Learning with Secret Sharing in Medical Computer Vision 701
Ruduan B.F. Plug, Putu H.P. Jati, Samson Y. Amare, and Mirjamvan Reisen

References 716
Index 721

Über Autor(innen)

Agbotiname Lucky Imoize is a Lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. He is a Fulbright Fellow, the Vice Chair of the IEEE Communication Society Nigeria chapter, and a Senior Member of IEEE.

Dinh-Thuan Do, PhD, is an Assistant Professor with the School of Engineering at the University of Mount Union, USA. He is an editor of IEEE Transactions on Vehicular Technology and Computer Communications. He is a Senior Member of IEEE.

Houbing Herbert Song, PhD, IEEE Fellow, is a Professor in the Department of Information Systems, and the Department of Computer Science and Electrical Engineering and Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab) at the University of Maryland, Baltimore County. He is also Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics.