# AI for the Internet of Things: Federated Learning, TinyML, and Intelligent Edge Devices
Status: public
Confidence: medium (0.8) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR
AI for IoT moves useful inference closer to sensors and devices. Federated learning helps train from distributed device data, TinyML runs small models on microcontrollers, and efficient model families such as MobileNets make edge vision practical under tight memory and latency budgets.

## Core Explanation
IoT systems collect data from cameras, wearables, industrial sensors, smart-home devices, and gateways. The main architectural choice is where intelligence runs: in the cloud, on a nearby edge gateway, or directly on the device. On-device inference reduces latency and can limit raw-data movement, but it requires small models, quantization, careful power use, and deployment tooling for constrained hardware.

## Detailed Analysis
Federated learning is useful when device data is sensitive or costly to centralize, because local clients train on local data and share updates for aggregation. TinyML focuses on inference in very small memory and power envelopes. Efficient CNN designs such as MobileNets support mobile and embedded vision by reducing computation while preserving enough accuracy for many practical tasks.

## Further Reading
- Google Research: Federated Learning
- TensorFlow Lite for Microcontrollers
- MobileNets on arXiv

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