Federated fine-tuning of foundation models for human activity recognition using wearable data under EU regulations

Abstract

Wearable health devices generate vast amounts of sensitive physiological data, creating opportunities for AI-driven health monitoring while raising significant privacy concerns under emerging European regulations. This thesis investigates the feasibility of federated learning for human activity recognition (HAR) using foundation models, specifically examining whether privacy-preserving approaches can maintain clinical accuracy while complying with GDPR, the EU AI Act, and the forthcoming European Health Data Space (EHDS). Three fine-tuning strategies are systematically compared for adapting the HARNet10 foundation model to the PAMAP2 dataset under both centralized and federated learning paradigms. Experiments reveal that the All-Layers-Except-BatchNorm (All-BN) strategy achieves optimal centralized performance, while classifier-only training provides a compelling balance of performance and stability. In federated settings implemented via PySyft, classifier-only training achieves just 3.33 percentage points below its centralized counterpart while maintaining identical effort-level classification accuracy (87.28%). The performance gap, though modest, is offset by the practical advantages of classifier-only adaptation, which offers minimal communication overhead, strong privacy preservation through local feature extraction, and compatibility with resource-constrained wearable devices. This work contributes with a reproducible benchmark for adapting foundation models under regulatory constraints and demonstrates that privacy-preserving federated training can deliver functional accuracy suitable for real-world health applications.