Federated IoT Node Kit — Build a Self-Contained ESP32 Device That Learns Locally and Shares Only Model Updates

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

This advanced kit places a federated learning experiment directly on your workbench. You will assemble and program an ESP32 that reads temperature, humidity, motion, and current data, runs a locally trained linear regression model, and periodically pushes only the model update deltas to a central MQTT broker. It’s a concrete implementation of edge AI collaboration—no simulated datasets, just real sensor streams and real model drift handled locally. Engineers, final-year students, and researchers exploring on-device training will find every hardware and firmware challenge mapped out.

What You'll Build

You’ll create a compact, enclosed sensor node that continuously monitors an environment and a load circuit. Two DHT22s and an MPU6050 capture ambient humidity, temperature, and vibration, while the ACS712 tracks current draw. A DS3231 RTC time-stamps every reading to the microSD card. The ESP32’s linear model infers a target value (e.g., anomaly score or future temperature) from these inputs. When enough local data accumulates, the node retrains the model, computes weight deltas, and publishes only those deltas via MQTT—not raw sensor data—preserving privacy and bandwidth. An OLED displays inference results and model version in real time.

What You'll Learn

• Design and implement a federated learning pipeline entirely on an ESP32, including on-device linear algebra and gradient descent
• Interface multiple I2C sensors simultaneously—DHT22, MPU6050, DS3231, and OLED—while resolving address conflicts and timing
• Structure MQTT topics and JSON payloads for efficient model delta exchange between edge nodes and a cloud server
• Assemble a permanent prototype using soldering, buck converter power regulation, and a sealed enclosure suitable for field deployment

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit targets final-year B.Tech students in ECE, EEE, and Computer Science working on federated learning or edge AI capstone projects. It’s equally suited for Smart India Hackathon teams needing a hardened edge node to demonstrate privacy-preserving ML. Researchers at IITs, NITs, and corporate R&D labs will appreciate the fully integrated hardware platform for running multi-node experiments. Prior experience with Arduino IDE and a basic understanding of linear algebra are expected.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.

Choose your assembly option:

• Soldering Kit — 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
• Breadboard Combo — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.