How to Build a Federated Learning System on Android Using Kotlin

Federated learning trains machine learning models locally on devices and sends only model updates to a central server, avoiding the need to share raw data. A tutorial published on DEV Community outlines how to build a federated learning prototype on Android using Kotlin, covering model download, local training, and update submission. The architecture relies on components such as a Model Manager, Training Engine, and Secure API Client, with Android's WorkManager handling background training tasks. Aggregation on the server uses Federated Averaging, combining weighted updates from multiple devices to produce an improved global model. The guide cautions that federated learning does not guarantee privacy on its own, and recommends additional measures such as HTTPS, device attestation, signed model artifacts, and server-side validation.