Federated learning for IoT — FedAvg vs FedProx vs FedOpt aggregation, secure aggregation, differential privacy budgets, and a 2026 deployment blueprint for edge fleets.
How Apple Intelligence works — A19 Neural Engine, Private Cloud Compute, attested ML servers, model routing, and the privacy-preserving AI architecture.
Step-by-step guide to running ML models on ESP32 using TensorFlow Lite Micro — quantization, memory budgeting, ESP-NN acceleration, and deployment patterns.
Complete engineering guide to deploying optimized AI models on edge hardware. Model quantization, TensorRT optimization, containerized inference pipelines, and fleet management at scale.