Federated Learning and TinyML for Localization: Benefits, Applications, and Challenges

Abstract

The growing demand for intelligent and context-aware Internet of Things (IoT) systems has spurred the advancement of machine learning (ML) solutions for real-time localization. Deploying ML models on distributed, resource-constrained edge devices offers substantial opportunities while posing unique challenges. This article presents a comprehensive review of strategies for adapting and designing ML models for deployment on embedded hardware with limited resources. We emphasize Tiny Machine Learning (TinyML), a paradigm that facilitates ultra-low-power ML execution on microcontrollers and sensors, and explore its synergy with Federated Learning (FL), which enables collaborative model training across devices without centralizing sensitive data. We examine training strategies, deployment workflows from cloud to edge, model optimization techniques, and key applications of TinyML and FL in localization. Benefits such as real-time processing, privacy preservation, and energy efficiency are analyzed, along with challenges including computational constraints, interoperability, and security vulnerabilities. The article concludes by identifying research gaps and outlining future directions for integrating TinyML and FL in large-scale localization systems.