Every user interacts with an app differently yet most apps treat everyone the same. What if your Android app could actually learn from each user and adapt over time, while still keeping their data safe? In this talk, I’ll share how to build privacy-first personalization into Android apps using simple, practical tools that are already available like ML Kit, TensorFlow Lite, and Android’s Smart Features APIs. We’ll look at how apps can track meaningful user behavior, store preferences securely, and use lightweight on-device models to deliver smarter recommendations or dynamic UI experiences. I’ll also touch on how federated learning concepts can help apps improve personalization collectively, without sharing raw user data. The goal is to show that AI-powered personalization doesn’t have to mean massive cloud systems it can be fast, local, and ethical. Key Takeaways: - Learn how to make Android apps adapt to users through on-device learning. - Understand how to collect user interaction data safely and transparently. - Explore ML Kit and TensorFlow Lite for real-world personalization use cases. - Apply privacy-first techniques using federated learning ideas. - See how responsible AI can boost both engagement and user trust.