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

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.

8 pages, 2024 International Conference on Intelligent Environments (IE), 2024

References in corpus (6)

• Federated Learning: Challenges, Methods, and Future Directions
• Federated Learning for Internet of Things: A Comprehensive Survey
• Learning Differentially Private Recurrent Language Models
• Federated Learning of a Mixture of Global and Local Models
• An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
• FLAME: Federated Learning Across Multi-device Environments