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Tiny Machine Learning: Design Principles and Applications
Agbotiname Lucky Imoize (Edited by), Imoize (Author), Dinh-Thuan Do (Edited by), Houbing Herbert Song (Edited by)
9781394294541, Wiley
Hardback, published 6 January 2026
784 pages
28 x 19 x 2.6 cm, 1.302 kg
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: Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.
About the Editors xxiii List of Contributors xxvii Preface xxxv 1 Introduction to TinyML 1 1.1 Introduction 1 1.2 Evolution of TinyML 6 1.3 Key Milestones and Current Trends 8 1.4 TinyML System Development 11 1.5 Challenges and Bottlenecks 17 1.6 Cost–Benefit Analysis 20 1.7 Key Findings 26 1.8 Limitations of TinyML 27 1.9 Conclusion 29 References 30 2 Learning Panorama Under TinyML 35 2.1 Introduction 35 2.2 Challenges and Opportunities for Improved TinyML Model Design 37 2.3 Frontiers in Model Optimization for TinyML 47 2.4 Learning Frameworks and Tools for TinyML Development 59 2.5 Frontiers for Algorithmic Innovations for TinyML 63 2.6 TinyML Development Process 68 2.7 Key Findings 69 2.8 Conclusion 70 References 71 3 TinyML for Anomaly Detection 85 3.1 Introduction 85 3.2 Context and Literature Review 93 3.3 Lessons Learned 149 3.4 Future Scope 152 3.5 Conclusion 156 References 157 4 TinyML Power Consumption and Memory in IoT MCUs 163 4.1 Introduction 163 4.2 Context and Literature Review 171 4.3 Methodology 174 4.4 Results and Discussion: Bibliometric Analysis 184 4.5 Conclusion and Future Directions 198 References 199 5 Efficient Data Cleaning and Anomaly Detection in IoT Devices Using TinyCleanEDF 205 5.1 Introduction 205 5.2 IoT and TinyCleanEDF 206 5.3 The Importance of Data Cleaning in IoT Systems 207 5.4 Anomaly Detection and Its Importance in IoT Systems 208 5.5 Data Preprocessing and Cleaning Workflow 209 5.6 Implementation Details 213 5.7 Case Studies and Applications 216 5.8 Future Directions 219 5.9 Conclusion and Future Scope 221 References 222 6 TinyML Devices and Tools 225 6.1 Introduction 225 6.2 Related Work 227 6.3 TinyML Devices 234 6.4 TinyML Tools 237 6.5 Deployment Procedure of TinyML 241 6.6 Lesson Learned and Prospects 250 6.7 Conclusion 253 References 254 7 Privacy-Preserving Techniques in TinyML for IoT 259 7.1 Introduction 259 7.2 Related Works 261 7.3 Homomorphic Encryption in TinyML 262 7.4 Differential Privacy for TinyML 271 7.5 Secure Multi-Party Computation in TinyML 277 7.6 Case Studies and Applications of Privacy-Preserving TinyML 281 7.7 Discussion and Future Directions 293 7.8 Conclusion 296 Acknowledgment 296 References 296 8 Enhancing Cybersecurity in TinyML with Lightweight Cryptographic Algorithms 303 8.1 Introduction 303 8.2 Literature Review 305 8.3 Lightweight Cryptographic Algorithms for TinyML 306 8.4 Comparative Analysis of Lightweight Block Ciphers 315 8.5 Comparative Analysis of Lightweight Hash Functions 318 8.6 Comparative Analysis of Lightweight Stream Ciphers 322 8.7 Implementation Strategies for Lightweight Cryptography in TinyML 325 8.8 Conclusion 327 Acknowledgment 328 References 329 9 Tiny Machine Learning for Enhanced Edge Intelligence 335 9.1 Introduction 335 9.2 Overview of Tiny Machine Learning (TinyML) 337 9.3 TinyML as a Service (TMLaaS) Architecture 349 9.4 Results and Discussion 354 9.5 Open Challenges and Further Research Directions 357 9.6 Conclusion 358 References 359 10 Advanced Security Schemes for TinyML Devices 367 10.1 Introduction 367 10.2 Fundamentals of TinyML 369 10.3 Privacy Concerns in TinyML 374 10.4 Security and Privacy Solutions 376 10.5 The Implementation of Novel Security Schemes for TinyML Applications 384 10.6 Privacy-Enhancing Techniques for TinyML 388 10.7 Future Directions of Security and Privacy in TinyML Devices 391 10.8 Lessons Learned 393 10.9 Limitations of this Study 394 10.10 Conclusion 395 References 396 11 Robust Ground Truth Data Mining for Enhanced Privacy and Accuracy in Noisy TinyML Environments 403 11.1 Introduction 403 11.2 Related Research Work 406 11.3 Models 408 11.4 Gdp 412 11.5 Evaluation 416 11.6 Discussion 420 11.7 Conclusions 423 References 424 12 Security and Privacy of TinyML Devices 431 12.1 Introduction 431 12.2 Related Work 433 12.3 Secure and Privacy-Aware Training of TinyML Models 437 12.4 Implementation of Novel Security Schemes for TinyML Applications 455 12.5 Lessons, Challenges, and Future Directions 463 12.6 Conclusions and Outlook 464 References 465 13 Semantic Management of TinyML for Industrial Application 469 13.1 Introduction 469 13.2 Introduction to TinyML 472 13.3 Recent Advances in TinyML 477 13.4 Methodology 488 13.5 Results and Discussion 495 13.6 Conclusions and Future Scope 498 References 498 14 Fight Poison with Poison: Tiny Machine Learning Resilience Against Poisoning Attacks 503 14.1 Introduction 503 14.2 Problem Definition 505 14.3 Related Work 507 14.4 Proposed Method 512 14.5 Evaluation Experiment 523 14.6 Discussion 541 14.7 Conclusion 543 References 544 15 TinyML for Real-Time Medical Image Classification and Diagnosis 549 15.1 Introduction 549 15.2 Literature Review 551 15.3 Methodology 566 15.4 Results and Discussion 567 15.5 Conclusion 578 References 578 16 Biometric Authentication in TinyML: Opportunities and Challenges 587 16.1 Introduction 587 16.2 Related Work 589 16.3 Overview of Biometric Authentication Techniques 591 16.4 Comparative Analysis of Biometric Authentication Methods 609 16.5 Adapting Biometric Techniques for TinyML Systems 615 16.6 Discussion and Future Directions 625 16.7 Conclusion 628 Acknowledgment 628 References 629 17 Secure Deployment of TinyML Applications: Strategies and Practices 635 17.1 Introduction 635 17.2 Related Work 636 17.3 Security Architectures for TinyML Deployments 638 17.4 Secure Bootstrapping and Key Management 643 17.5 Secure Communication Protocols for TinyML 646 17.6 Data Privacy in TinyML Applications 650 17.7 Future Directions and Emerging Technologies 652 17.8 Conclusion 655 Acknowledgment 657 References 657 18 TinyML for Environmental Applications 665 18.1 Introduction 665 18.2 Related Work 667 18.3 Methodology 669 18.4 Case Study: New Insights on Air Writing from Pławiak and Alblehai 689 18.5 Results and Discussion 692 18.6 Conclusion and Future Scope 697 References 697 19 Benchmarking TinyML Encrypted Federated Learning with Secret Sharing in Medical Computer Vision 701 19.1 Introduction 701 19.2 Related Work 702 19.3 Methodology 703 19.4 Results 711 19.5 Conclusion 715 References 716 Index 721
Francisca Onyiyechi Nwokoma, Chidi Ukamaka Betrand, Juliet Nnenna Odii, Euphemia Chioma Nwokorie, and Ikechukwu Ignatius Ayogu
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Richard Govada Joshua, Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, and Samuel Oluwatobi Tofade
Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, Richard Govada Joshua, and Samuel Oluwatobi Tofade
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Abeeb Akorede Bello, Agbotiname Lucky Imoize, and Abiodun Temitope Odewale
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Wasswa Shafik and Mumin Adam
Yuichi Sei and Agbotiname Lucky Imoize
Eftychia Mistillioglou, Evangelia Konstantopoulou, Nicolas Sklavos, and Andronikos Kyriakou
Kinzah Noor, Hasnain Ahmad, and Agbotiname Lucky Imoize
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Jelil O. Agbo-Ajala, Lateef A. Akinyemi, Olufisayo S. Ekundayo, and Ernest Mnkandla
Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Oleksii Smirnov, and Boris Khruskov
Oleksandr Kuznetsov, Sergii Kavun, and Gulvira Bekeshova
Duy Nam Khanh Vu and Anh Khoa Dang
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Subject Areas: Electronics & communications engineering [TJ]
