TinyML-Based Federated Learning: A Novel Framework for Privacy-Preserving Smart Healthcare Applications

Abstract

This paper presents an optimized integration framework combining Tiny Machine Learning (TinyML) and Federated Learning (FL) for privacy-preserving smart healthcare applications. While building upon established techniques, our contribution lies in their synergistic adaptation and optimization for resource-constrained healthcare Internet of Things (IoT) environments. We implement Adaptive Noise Injection (ANI) with data-sensitive tuning and Authenticated Homomorphic Encryption (AHE) using the Cheon-Kim-Kim-Song (CKKS) scheme to create a multi-layered privacy shield. Experimental validation using synthetic Electronic Health Record (EHR) data (derived from real Indonesian hospital patterns) demonstrates an effective privacy-utility balance, achieving 89% classification accuracy with differential privacy (ε = 1.0, σ = 0.01). The framework maintains inference latency under 60 ms with only 5% estimated daily battery consumption on typical wearable hardware.