Privacy-preserving IoT intrusion detection system combining TinyML inference on edge devices with federated learning and blockchain tamper-evidence. Raw sensor data from smart cameras, thermostats, and sensor nodes flows through feature extraction and TensorFlow Lite quantized models on a Raspberry Pi 4 gateway, generating local alerts logged to JSON. Flower federated learning framework trains model weights across distributed edge nodes without centralizing raw data, while Merkle tree hashing and Ethereum smart contracts anchor detection logs to an immutable ledger. This architecture demonstrates zero-trust IoT security: raw data never leaves the edge, only model weights and cryptographic proofs traverse the cloud and blockchain layers. Fork and customize this diagram on Diagrams.so to adapt threat models, add additional sensor types, or integrate with AWS IoT Core and SageMaker for production deployments.