Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors

• URL: http://arxiv.org/abs/2512.03287v1
• Date: Tue, 02 Dec 2025 22:52:58 GMT
• Title: Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
• Authors: Dario Fenoglio, Mohan Li, Davide Casnici, Matias Laporte, Shkurta Gashi, Silvia Santini, Martin Gjoreski, Marc Langheinrich,
• Abstract summary: This work proposes multi-frequency Federated Learning (FL) to enable privacy-aware ML.<n>We focus on head-worn devices (e.g., earbuds and smart glasses), a relatively unexplored domain compared to traditional smartwatch- or smartphone-based HAR.<n>Results have shown improvements on two datasets against frequency-specific approaches.
• Score: 13.34990751054306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of user data to a centralized server. This work proposes multi-frequency Federated Learning (FL) to enable: (1) privacy-aware ML; (2) joint ML model learning across devices with varying sampling frequency. We focus on head-worn devices (e.g., earbuds and smart glasses), a relatively unexplored domain compared to traditional smartwatch- or smartphone-based HAR. Results have shown improvements on two datasets against frequency-specific approaches, indicating a promising future in the multi-frequency FL-HAR task. The proposed network's implementation is publicly available for further research and development.

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