On-Device ML Personalization Workflow: From User Input to Adapted Models

Abstract

This article addresses the significant accessibility gap between enterprise and independent developers in implementing machine learning personalization features on mobile devices. It presents an open-source Swift/Kotlin toolkit that democratizes on-device ML by providing a unified cross-platform framework, reducing implementation complexity while eliminating cloud dependencies. The article details the technical architecture, including 8-bit transfer learning methodology, background processing framework, and secure federated averaging protocol. Through case studies in handwriting recognition and voice command assistance, it demonstrates the toolkit's effectiveness in real-world applications. The article also explores user experience design considerations for on-device learning, including consent frameworks, progress indicators, and interface elements that enhance user retention. Finally, it provides deployment guidelines for different device categories, graceful degradation strategies, app store compliance considerations, and outlines promising research opportunities for community contributions. The approach enables indie developers to implement sophisticated personalization features previously accessible only to teams with specialized ML expertise and infrastructure.