Privacy-First AI: Building a Federated Health Tracker with Flower and Scikit-Learn

In the era of wearable tech, our devices know more about our health than we do. But here’s the billion-dollar dilemma: how do we train a high-performing Federated Learning model to predict sleep quality without compromising user privacy? Sending raw medical data to a central cloud is a massive security risk and a regulatory nightmare. Today, we are diving deep into Privacy-Preserving AI using the Flower (flwr) framework and Scikit-learn. We will build an Edge AI system that learns from decentralized health data across multiple simulated devices. By leveraging gRPC for communication and Docker for orchestration, we ensure that sensitive biological markers never leave the "device," keeping data ownership where it belongs—with the user. Why Federated Learning? Traditional Machine Learning requires data to be centralized. Federated Learning (FL) flips the script. Instead of bringing data to the code, we bring the code to the data. The Architecture The following diagram illustrates how the