ELEMENTS OF MACHINE DESIGN
CONTENTS INTRODUCTION TO TINYML INTRODUCTION KEY CONTRIBUTIONS OF THE CHAPTER CHAPTER ORGANIZATION EVOLUTION OF TINYML KEY MILESTONES AND CURRENT TRENDS TINYML SYSTEM DEVELOPMENT HARDWARE PLATFORMS SOFTWARE FRAMEWORKS CHALLENGES AND BOTTLENECKS COST–BENEFIT ANALYSIS KEY FINDINGS LIMITATIONS OF TINYML CONCLUSION REFERENCES LEARNING PANORAMA UNDER TINYML CHAPTER CONTRIBUTION CHALLENGES AND OPPORTUNITIES FOR IMPROVED TINYML MODEL DESIGN NEURAL ARCHITECTURE SEARCH NEURAL ATTENTION MECHANISMS DEPTH-SEPARABLE CONVOLUTION RATIONAL ACTIVATION FUNCTIONS FRONTIERS IN MODEL OPTIMIZATION FOR TINYML PRUNING QUANTIZATION LOW-RANK DECOMPOSITION KNOWLEDGE DISTILLATION LEARNING FRAMEWORKS AND TOOLS FOR TINYML DEVELOPMENT FRONTIERS FOR ALGORITHMIC INNOVATIONS FOR TINYML MULTIMODAL MACHINE LEARNING FOR TINYML META-LEARNING ALGORITHMS FOR TINYML TINYML DEVELOPMENT PROCESS TINYML FOR ANOMALY DETECTION RESEARCH CONCEPTS AND MOTIVATION IMPORTANCE OF TINYML IN ANOMALY DETECTION KEY CONTRIBUTIONS CONTEXT AND LITERATURE REVIEW TINYML: AN OVERVIEW DEFINITION AND KEY FEATURES ADVANTAGES OF TINYML FOR EDGE DEVICES LIMITATIONS AND CHALLENGES OF TINYML HARDWARE CONSTRAINTS MODEL OPTIMIZATION CHALLENGE DATA AVAILABILITY AND PREPROCESSING CONSTRAINTS DEPLOYMENT CHALLENGES DEVELOPMENT AND TESTING CHALLENGES RELATED WORK ANOMALY DETECTION IN IOT AND EDGE DEVICES STRUCTURED LITERATURE MAPPING APPROACH UNDERSTANDING PICOC IN THE CONTEXT OF SYSTEMATIC LITERATURE MAPPING FORMULATING RESEARCH QUESTIONS BASED ON THE PICOC FRAMEWORK INCLUSION/EXCLUSION CRITERIA SEARCH STRATEGY DATA EXTRACTION PROCESS DISTRIBUTION OF RESEARCH STUDIES CASE STUDY 1: INDUSTRIAL ANOMALY DETECTION USING EDGE COMPUTING AND TINYML CASE STUDY 2: TINYML FOR ANOMALY DETECTION IN INDUSTRIAL EQUIPMENT MONITORING CASE STUDY 3: TINYML FOR ANOMALY DETECTION IN SMART AGRICULTURE CASE STUDY 4: TINYML FOR ANOMALY DETECTION IN SMART HOMES CASE STUDY 4: TINYML FOR ANOMALY DETECTION IN WEARABLE HEALTH DEVICES MACHINE LEARNING MODELS FOR ANOMALY DETECTION TINYML APPLICATIONS IN ANOMALY DETECTION PRACTICAL APPLICATIONS OF TINYML IN ANOMALY DETECTION INDUSTRIAL MACHINERY MONITORING INFRASTRUCTURE HEALTH MONITORING CYBERSECURITY IN IOT NETWORKS ENVIRONMENTAL MONITORING HEALTHCARE AND WEARABLE DEVICES SMART CITIES AND TRANSPORTATION SYSTEMS LESSONS LEARNED ADAPTABILITY OF MODELS TO CONSTRAINED ENVIRONMENTS IMPORTANCE OF REAL-TIME PROCESSING BALANCING PRECISION AND EFFICIENCY DATA QUALITY AND PREPROCESSING PRIVACY AND SECURITY CONCERNS SCALABILITY AND DEPLOYMENT CHALLENGES CONTINUOUS LEARNING AND ADAPTATION COLLABORATIVE AND INTERDISCIPLINARY APPROACHES TRANSFORMATIVE POTENTIAL OF TINYML OPEN CHALLENGES FUTURE SCOPE MODEL OPTIMIZATION AND TRANSFER LEARNING ENERGY-EFFICIENT ALGORITHMS FEDERATED AND COLLABORATIVE LEARNING REAL-TIME AND ADAPTIVE DETECTION MULTIMODAL DATA FUSION SECURITY AND ROBUSTNESS INTEGRATION WITH EMERGING TECHNOLOGIES APPLICATIONS IN NEW DOMAINS TINYML POWER CONSUMPTION AND MEMORY IN IOT MCUS BACKGROUND AND CONTEXT OF IOT MCUS EMERGENCE OF TINYML IN IOT SYSTEMS POWER CONSUMPTION CHALLENGES IN IOT MCUS MEMORY CONSTRAINT IN IOT MCUS ULTRALOW-POWER SMART IOT DEVICES WITH EMBEDDED TINYML DATA MANAGEMENT TECHNIQUES UTILIZING TINYML IN IOT SYSTEMS TINYML FOR REAL-TIME LOW-POWER IOT CIRCUIT MODELING TINYML FOR ULTRALOW-POWER AI IN LARGE-SCALE IOT DEPLOYMENTS RESEARCH CONTRIBUTIONS POWER CONSUMPTION IN IOT MCUS MEMORY CONSTRAINTS IN IOT MCUS INTEGRATION OF POWER AND MEMORY OPTIMIZATION TECHNIQUES COMPARATIVE ANALYSIS OF MCU ARCHITECTURES FOR IOT METHODOLOGY RESEARCH DESIGN: LITERATURE REVIEW FRAMEWORK DEFINING THE SCOPE OF THE LITERATURE REVIEW DEVELOPING SELECTION CRITERIA INCLUSION AND EXCLUSION CRITERIA SEARCH STRATEGY IMPLEMENTATION DATA EXTRACTION AND ORGANIZATION SYNTHESIZING FINDINGS REVIEW PROCESS AND ANALYSIS RESULTS AND DISCUSSION: BIBLIOMETRIC ANALYSIS PUBLICATION TRENDS COLLABORATIVE NETWORKS THEMATIC FOCUS AREAS INSIGHTS FROM VISUALIZATION DISCUSSIONS CONCLUSION AND FUTURE DIRECTIONS EF?CIENT DATA CLEANING AND ANOMALY DETECTION IN IOT DEVICES USING TINYCLEANEDF IOT AND TINYCLEANEDF THE IMPORTANCE OF DATA CLEANING IN IOT SYSTEMS ANOMALY DETECTION AND ITS IMPORTANCE IN IOT SYSTEMS ANOMALY DETECTION DATA PREPROCESSING AND CLEANING WORKFLOW NOISE REMOVAL TECHNIQUES HANDLING MISSING DATA ANOMALY DETECTION FRAMEWORK STATISTICAL APPROACHES MACHINE LEARNING ALGORITHMS FEDERATED LEARNING INTEGRATION DISTRIBUTED MODEL TRAINING DATA PRIVACY MECHANISMS SCALABILITY AND COMPATIBILITY ENHANCEMENTS IMPLEMENTATION DETAILS SOFTWARE AND HARDWARE REQUIREMENTS SOFTWARE REQUIREMENTS HARDWARE REQUIREMENTS SYSTEM ARCHITECTURE OF TINYCLEANEDF DATA ACQUISITION LAYER LOCAL PROCESSING LAYER FEDERATED AGGREGATION LAYER COMMUNICATION LAYER DEPLOYMENT ON IOT DEVICES PRE-DEPLOYMENT PREPARATION INSTALLATION AND INITIALIZATION OPERATIONAL PHASE CASE STUDIES AND APPLICATIONS FUTURE DIRECTIONS ENHANCEMENTS IN FEDERATED LEARNING ALGORITHMS INTEGRATION WITH EDGE AI TECHNOLOGIES EXPANDING TO OTHER IOT USE CASES CONCLUSION AND FUTURE SCOPE TINYML DEVICES AND TOOLS TINYML DEVICES ARCHITECTURE AND FUNCTIONALITY TINYML TOOLS TENSORFLOW LITE FOR MICROCONTROLLERS (TFLM) EDGE IMPULSE PYTORCH MOBILE TENSORFLOW LITE (TFL) UTENSOR CMSIS-NN STM32CUBE AI AND NANOEDGE STUDIO EMBML FANN ON MCU DEPLOYMENT PROCEDURE OF TINYML CASE STUDIES OF TINYML IN THE HEALTH SECTOR DOMAIN CASE STUDIES OF TINYML IN THE AGRICULTURE DOMAIN IRRIGATION CONTROLLER LESSON LEARNED AND PROSPECTS LESSON LEARNED PROSPECTS PRIVACY-PRESERVING TECHNIQUES IN TINYML FOR IOT RELATED WORKS HOMOMORPHIC ENCRYPTION IN TINYML FUNDAMENTALS OF HOMOMORPHIC ENCRYPTION PARTIALLY AND FULLY HOMOMORPHIC ENCRYPTION SCHEMES APPLYING HOMOMORPHIC ENCRYPTION TO TINYML TASKS PERFORMANCE EVALUATION AND PRACTICAL CONSIDERATIONS COMPARISON OF HOMOMORPHIC ENCRYPTION SCHEMES DIFFERENTIAL PRIVACY FOR TINYML PRINCIPLES OF DIFFERENTIAL PRIVACY MECHANISMS FOR ACHIEVING DIFFERENTIAL PRIVACY DIFFERENTIALLY PRIVATE TINYML ALGORITHMS BALANCING PRIVACY AND UTILITY IN TINYML APPLICATIONS SECURE MULTI-PARTY COMPUTATION IN TINYML FOUNDATIONS OF SECURE MULTI-PARTY COMPUTATION SECRET SHARING AND GARBLED CIRCUITS SECURE AGGREGATION FOR DISTRIBUTED TINYML EFFICIENCY OPTIMIZATION FOR SECURE MULTI-PARTY COMPUTATION IN TINYML CASE STUDIES AND APPLICATIONS OF PRIVACY-PRESERVING TINYML EFFICIENT SECURE MULTI-PARTY COMPUTATION FOR PRIVACY-PRESERVING MACHINE LEARNING A GENERIC FRAMEWORK FOR PRIVACY-PRESERVING DEEP LEARNING MALICIOUSLY SECURE COOPETITIVE LEARNING FOR LINEAR MODELS PRACTICAL SECURE AGGREGATION FOR PRIVACY-PRESERVING MACHINE LEARNING FALCON: HONEST MAJORITY MALICIOUSLY SECURE FRAMEWORK FOR PRIVATE DEEP LEARNING DISCUSSION AND FUTURE DIRECTIONS SECURE MULTI-PARTY COMPUTATION FOR TINYML PRACTICAL CONSIDERATIONS AND FUTURE DIRECTIONS ACKNOWLEDGMENT ENHANCING CYBERSECURITY IN TINYML WITH LIGHTWEIGHT CRYPTOGRAPHIC ALGORITHMS LITERATURE REVIEW LIGHTWEIGHT CRYPTOGRAPHIC ALGORITHMS FOR TINYML BLOCK CIPHERS PRESENT SIMON AND SPECK GIFT HASH FUNCTIONS PHOTON SPONGENT QUARK STREAM CIPHERS TRIVIUM GRAIN MICKEY COMPARATIVE ANALYSIS OF LIGHTWEIGHT BLOCK CIPHERS SECURITY LEVELS AND RESISTANCE TO KNOWN ATTACKS COMPUTATIONAL EFFICIENCY AND RESOURCE UTILIZATION ENERGY CONSUMPTION PROFILES MEMORY FOOTPRINT ANALYSIS LATENCY AND THROUGHPUT CONSIDERATIONS COMPARATIVE ANALYSIS OF LIGHTWEIGHT HASH FUNCTIONS COMPARATIVE ANALYSIS OF LIGHTWEIGHT STREAM CIPHERS IMPLEMENTATION STRATEGIES FOR LIGHTWEIGHT CRYPTOGRAPHY IN TINYML HARDWARE ACCELERATION TECHNIQUES SOFTWARE OPTIMIZATION METHODS HYBRID APPROACHES LEVERAGING BOTH HARDWARE AND SOFTWARE OPTIMIZATIONS TINY MACHINE LEARNING FOR ENHANCED EDGE INTELLIGENCE OVERVIEW OF TINY MACHINE LEARNING (TINYML) CHALLENGES OF LARGE MACHINE LEARNING IN EDGE INTELLIGENCE RESOURCE CONSTRAINTS IN EDGE DEVICES LATENCY AND REAL-TIME INFERENCE REQUIREMENTS SECURITY AND PRIVACY CONCERNS HETEROGENEITY IN EDGE DEVICES AND SCALABILITY TINYML WORKFLOW IMPLEMENTATION TINYML IMPLEMENTATION AND TF-LITE MICRO DESIGN PRINCIPLES TINYML TECHNIQUES FOR ENHANCED EDGE INTELLIGENCE MODEL OPTIMIZATION TECHNIQUES EFFICIENT NEURAL NETWORK ARCHITECTURES ON-DEVICE LEARNING AND ADAPTATION ADAPTIVE INFERENCE AND DYNAMIC MODEL SCALING TINYML AS A SERVICE (TMLAAS) ARCHITECTURE ARCHITECTURAL CONSIDERATIONS IN TMLAAS ENERGY AND COMPUTATIONAL TRADE-OFFS SECURITY AND PRIVACY TRADE-OFFS MODALITY OF COMMUNICATION MLOPS IN TMLAAS ARCHITECTURE CASE STUDY RESULTS AND DISCUSSION OPEN CHALLENGES AND FURTHER RESEARCH DIRECTIONS TINYML FOR TRANSFER LEARNING TINYML FOR FEDERATED LEARNING PERFORMANCE AND SCALABILITY TRUSTWORTHINESS OF PRETRAINED MODELS IN TRANSFER LEARNING SECURE MODEL-SHARING PROTOCOLS ADVANCED SECURITY SCHEMES FOR TINYML DEVICES THE CHAPTER CONTRIBUTION THE CHAPTER ORGANIZATION FUNDAMENTALS OF TINYML SECURITY THREATS IN TINYML DEVICES PHYSICAL ATTACKS SOFTWARE ATTACKS SIDE-CHANNEL ATTACKS PRIVACY CONCERNS IN TINYML DATA PRIVACY AND PROTECTION USER PRIVACY AND CONSENT ISSUES SECURITY AND PRIVACY SOLUTIONS HARDWARE-BASED SOLUTIONS SOFTWARE-BASED SOLUTIONS ENCRYPTION AND AUTHENTICATION TECHNIQUES THE IMPLEMENTATION OF NOVEL SECURITY SCHEMES FOR TINYML APPLICATIONS INTRUSION DETECTION SYSTEM (IDS) TRUSTED EXECUTION ENVIRONMENT (TEE) LIGHTWEIGHT CRYPTOGRAPHY PRIVACY-ENHANCING TECHNIQUES FOR TINYML FEDERATED LEARNING DIFFERENTIAL PRIVACY (DP) HOMOMORPHIC ENCRYPTION (HE) FUTURE DIRECTIONS OF SECURITY AND PRIVACY IN TINYML DEVICES SECURE MODEL INFERENCE AND EXECUTION ADVERSARIAL ATTACK AND DEFENSE MECHANISMS FEDERATED LEARNING (FL) WITH DIFFERENTIAL PRIVACY SECURITY TAMPER-RESISTANT HARDWARE FOR TINYML ENERGY-EFFICIENT SECURITY PROTOCOLS REAL-TIME SECURITY MONITORING AND ANOMALY DETECTION LIMITATIONS OF THIS STUDY ROBUST GROUND TRUTH DATA MINING FOR ENHANCED PRIVACY AND ACCURACY IN NOISY TINYML ENVIRONMENTS RELATED RESEARCH WORK DP AND TINYML ANONYMIZED DATA COLLECTION FOR TINYML PRIVACY-PRESERVING METHODS ACCOUNTING FOR SENSING NOISES DP METHODS CONSIDERING DIRTY DATA MODELS APPLICATION MODEL ASSUMPTIONS ATTACK MODEL PRIVACY METRIC GDP NUMERICAL VALUE ANONYMIZATION CATEGORICAL ATTRIBUTES EVALUATION PARAMETER SETTINGS VALUE OF ?? VALUE OF ?? VALUE OF PI UTILITY METRIC REAL DATASET RESULTS COMPUTATION COST OVERHEAD COST DISCUSSION TINYML WITH NOISY IOT DATA TINYML AND DP IMPLEMENTING DP ON EDGE DEVICES USE CASES AND APPLICATIONS TECHNICAL CHALLENGES AND SOLUTIONS FUTURE WORK CONCLUSIONS SECURITY AND PRIVACY OF TINYML DEVICES MAIN CONTRIBUTIONS SECURE AND PRIVACY-AWARE TRAINING OF TINYML MODELS FEDERATED LEARNING FOR PRIVACY-PRESERVING TRAINING IN TINYML INTELLECTUAL PROPERTY PROTECTION IN TINYML ADVERSARIAL ROBUSTNESS IN TINYML MODELS DISTRIBUTED FEDERATED LEARNING (D-FEDAVG) FOR ENHANCED PRIVACY SECURITY-AWARE QUANTIZATION IN TINYML FEDERATED TRANSFORMED LEARNING AND MODEL RESILIENCY USE CASES SMART HEALTHCARE: PRIVATE AND SECURE PATIENT MONITORING INDUSTRIAL IOT MONITORING: SECURE PREDICTIVE MAINTENANCE SMART AGRICULTURE: PRIVACY-CONSCIOUS ENVIRONMENTAL MONITORING SMART CITY INFRASTRUCTURE: PRIVACY-AWARE TRAFFIC AND POLLUTION MONITORING CONSUMER ELECTRONICS: PRIVACY-AWARE VOICE ASSISTANTS AND SMART HOME DEVICES IMPLEMENTATION OF NOVEL SECURITY SCHEMES FOR TINYML APPLICATIONS DETECTABILITY ASSESSMENT FOR IOT-RELATED CYBER ATTACKS: ADAPTING THE D-SCORE METHODOLOGY HARDWARE-BASED PROTECTION FOR TINYML AGAINST PHYSICAL AND LOGICAL THREATS NONINVASIVE ATTACK COUNTERMEASURES USING SIGNAL OBFUSCATION PHYSICALLY UNCLONABLE FUNCTIONS (PUFS) FOR SECURE AUTHENTICATION HARDWARE COUNTERMEASURES AVAILABLE ON COMMODITY MICROCONTROLLERS IMPLEMENTATION OF LIGHTWEIGHT ENCRYPTION FOR SECURE MEMORY ACCESS INTELLIGENT EDGE-ENABLED SECURITY AND BLOCKCHAIN INTEGRATION FOR TINYML APPLICATIONS COMPREHENSIVE SECURITY STRATEGIES FOR TINYML IN IOT ENVIRONMENTS LESSONS, CHALLENGES, AND FUTURE DIRECTIONS CONCLUSIONS AND OUTLOOK SEMANTIC MANAGEMENT OF TINYML FOR INDUSTRIAL APPLICATION RECENT ADVANCES IN TINYML LATEST HARDWARE TRENDS EMPOWERING TINYML TINYML WORKFLOWS ML-FOCUSED PROCESS HARDWARE (HW)-FOCUSED PROCESS CO-DESIGN PROCESS INSIGHTS ON LEARNING THROUGH THE TINYML PERSPECTIVE DEVICES AND TOOLS FOR TINYML SOFTWARE PLATFORMS TINYML CONSTRAINTS MOBILE-OPTIMIZED MODELS ARE UNSUITABLE FOR TINYML CHALLENGES IN ADAPTING INFERENCE MODELS FOR TINY TRAINING TINYML APPLICATION TINYML AND LARGE ML COMPARISON DESIGNING A SEMANTICS FOR TINYML MODELS SEMANTIC FRAMEWORK FOR TINYML NEURAL NETWORKS SEMANTIC KNOWLEDGE GRAPH (KG) FOR TINYML MODEL TINYENGINE: A COMPACT AND MEMORY-OPTIMIZED INFERENCE LIBRARY CONCLUSIONS AND FUTURE SCOPE FIGHT POISON WITH POISON: TINY MACHINE LEARNING RESILIENCE AGAINST POISONING ATTACKS PROBLEM DEFINITION ASSUMED ENVIRONMENT THREAT MODEL POISONING ATTACKS DEFENSIVE METHODS AGAINST POISONING ATTACKS OTHER ATTACKS ON TINYML PROPOSED METHOD OVERVIEW NEGATIVE IMPACT ADVERSARIAL IMPACT DATA REMOVAL REMOVAL PRIORITY EVALUATION EXPERIMENT MNIST EXPERIMENTAL SETTING RESULTS SYNTHETIC DATA ENVIRONMENTAL SENSOR TELEMETRY DATA ATTACK METHODS UTILIZED ADVERSARIAL IMPACT OF EACH DEVICE WHEN HARMFUL DATA CONSTITUTES THE MAJORITY AGAINST ATTACKERS AWARE OF THE DEFENSE METHOD’S INTERNAL STRUCTURE LIMITATIONS TINYML FOR REAL-TIME MEDICAL IMAGE CLASSI?CATION AND DIAGNOSIS METHODS EMPLOYED IN MEDICAL IMAGE CLASSIFICATION AND DIAGNOSIS TINYML MODELS AND ARCHITECTURE USED IN REAL-TIME MEDICAL IMAGE CLASSIFICATION AND DIAGNOSIS APPLICATION AREAS OF TINYML IN MEDICAL IMAGE CLASSIFICATION AND DIAGNOSIS MACHINE LEARNING (ML) DEEP LEARNING (DL) TINYML DESCRIPTION OF DATASETS EMPLOYED IN THIS WORK MODEL EVALUATION METRICS RESULTS OF KEY FINDINGS LESSONS LEARNED AND FUTURE RESEARCH DIRECTION BIOMETRIC AUTHENTICATION IN TINYML: OPPORTUNITIES AND CHALLENGES OVERVIEW OF BIOMETRIC AUTHENTICATION TECHNIQUES FINGERPRINT RECOGNITION FACIAL RECOGNITION IRIS SCANNING OTHER EMERGING BIOMETRIC MODALITIES VOICE RECOGNITION GAIT RECOGNITION EAR RECOGNITION COMPARATIVE ANALYSIS OF BIOMETRIC AUTHENTICATION METHODS PERFORMANCE METRICS FOR BIOMETRIC AUTHENTICATION FALSE ACCEPTANCE RATE (FAR) FALSE REJECTION RATE (FRR) EQUAL ERROR RATE (EER) RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE FAILURE TO ENROLL (FTE) RATE THROUGHPUT RATE USER ACCEPTANCE RATE ADAPTING BIOMETRIC TECHNIQUES FOR TINYML SYSTEMS CONSTRAINTS AND CHALLENGES OF MICROCONTROLLER-BASED SYSTEMS RESOURCE-EFFICIENT BIOMETRIC ALGORITHMS FOR TINYML HARDWARE CONSIDERATIONS FOR BIOMETRIC SENSORS IN IOT DEVICES SECURE DEPLOYMENT OF TINYML APPLICATIONS: STRATEGIES AND PRACTICES SECURITY ARCHITECTURES FOR TINYML DEPLOYMENTS LIGHTWEIGHT CRYPTOGRAPHIC PRIMITIVES FOR CONSTRAINED DEVICES SECURE ENCLAVES AND TRUSTED EXECUTION ENVIRONMENTS HARDWARE-BASED SECURITY FEATURES IN MODERN MICROCONTROLLERS COMPARISON OF SECURITY ARCHITECTURES SECURE BOOTSTRAPPING AND KEY MANAGEMENT ROOT OF TRUST IMPLEMENTATION IN TINYML DEVICES SECURE BOOT PROCESSES FOR MICROCONTROLLERS KEY GENERATION AND STORAGE TECHNIQUES SECURE COMMUNICATION PROTOCOLS FOR TINYML LIGHTWEIGHT TLS IMPLEMENTATIONS SECURE MQTT FOR IOT ENVIRONMENTS NOVEL PROTOCOLS DESIGNED FOR CONSTRAINED DEVICES PERFORMANCE COMPARISON OF SECURE COMMUNICATION PROTOCOLS DATA PRIVACY IN TINYML APPLICATIONS LOCAL DATA PROCESSING AND EDGE ANALYTICS DIFFERENTIAL PRIVACY TECHNIQUES FOR TINYML FEDERATED LEARNING APPROACHES FOR PRIVACY PRESERVATION FUTURE DIRECTIONS AND EMERGING TECHNOLOGIES POST-QUANTUM CRYPTOGRAPHY FOR TINYML ENHANCING SECURITY THROUGH ADVANCED HARDWARE LEVERAGING FEDERATED LEARNING FOR PRIVACY PRESERVATION EXPLOITING BLOCKCHAIN FOR ENHANCED INTEGRITY AND ACCOUNTABILITY ADAPTIVE SECURITY POLICIES ETHICAL CONSIDERATIONS AND AI GOVERNANCE RESEARCH CHALLENGES AND OPPORTUNITIES INTERDISCIPLINARY COLLABORATION SUMMARY OF KEY STRATEGIES FOR SECURE TINYML DEPLOYMENT THE EVOLVING LANDSCAPE OF TINYML SECURITY CALL TO ACTION FOR RESEARCHERS AND PRACTITIONERS TINYML FOR ENVIRONMENTAL APPLICATIONS SMART AGRICULTURE MACHINE LEARNING METHODS NEURAL NETWORKS AND STRUCTURE DESIGN HEALTHCARE SYSTEMS NEURAL NETWORKS AND DESIGNING STRUCTURE MILITARY SYSTEMS WIRELESS SYSTEMS CASE STUDY: NEW INSIGHTS ON AIR WRITING FROM P?AWIAK AND ALBLEHAI CASE STUDY – DATASET CASE STUDY – PREPROCESSING, TRAINING, AND TESTING MODEL DISCUSSION AND CONCLUSION SMART AGRICULTURE RESULTS AND DISCUSSION HEALTHCARE SYSTEM RESULTS AND DISCUSSION MILITARY SYSTEM RESULTS AND DISCUSSION WIRELESS SYSTEM RESULTS AND DISCUSSION BENCHMARKING TINYML ENCRYPTED FEDERATED LEARNING WITH SECRET SHARING IN MEDICAL COMPUTER VISION MEDMNIST DATASET RESNET18 MODEL ARCHITECTURE FEDERATED LEARNING ARCHITECTURE ENCRYPTION AND SECRET SHARING EXPERIMENTAL SETUP