#Internet of Things (IoT) & Smart Devices

• Tiny Transformers on the Edge: How TinyML and Efficient AI Architectures Are Democratizing On-Device Intelligence for IoT
• Tiny On-Device LLMs: Privacy-First AI at the Edge for IoT and Smart Devices
• TinyML on the Edge: How micro-LLMs on consumer IoT devices enable private, real-time AI without cloud access
• Self-Sovereign AI for Wearables: A Privacy-First Data Economy with On-Device AI, Federated Learning, and Blockchain
• Tiny Transformers on the Edge: Practical pathways to private, low-latency AI inference for IoT and mobile devices
• TinyML at the Edge: A practical blueprint for on-device anomaly detection to secure IoT devices with privacy-preserving AI in 2025
• On-Device AI for Real-Time Threat Detection: Edge ML Strategies to Secure IoT Devices Without Cloud Latency
• On-device Federated Learning for IoT Security: A practical blueprint for privacy-preserving, real-time threat detection at the edge
• Tiny LLMs on the Edge: A practical blueprint for running on-device AI in consumer IoT devices without cloud latency
• Tiny Transformers on the Edge: A practical blueprint for running domain-specific LLM inference on smartphones and edge devices using quantization, pruning, and privacy-preserving techniques
• On-device AI for Wearables: Federated, Privacy-Preserving Anomaly Detection
• Autonomous AI Agents at the Edge for IoT: Architectures, Safety, and Developer Workflows
• TinyML for Healthcare: On-device, privacy-preserving diagnostic inference for rural clinics powered by edge AI
• Federated Learning at City Scale: Privacy-Preserving Edge AI for Real-Time Traffic Optimization
• On-device Federated Learning for IoT Security: A Practical Blueprint for Lightweight Edge AIs to Detect Threats Without Transmitting Raw Data
• On-device Federated Learning for Consumer IoT: TinyML, Edge AI, and Privacy-First Personalization Without Cloud
• TinyML on the Edge: On-device Anomaly Detection and Autonomous Response for IoT Security
• Federated Learning at the Edge: Privacy-Preserving AI for Real-Time IoT Security in 5G/6G Smart Cities
• TinyML on Microcontrollers: Building Privacy-Preserving On-Device AI for Smart Home Sensor Networks
• TinyML on the Edge: Deploying Ultra-efficient AI on Battery-Powered IoT in 2025 — From Compression to Federated Learning
• Federated Learning at the Edge: Privacy-Preserving Health Insights from Wearables