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FedOpenHAR: Federated Multitask Transfer Learning for Sensor-Based Human Activity Recognition.

Egemen İşgÜder¹, Özlem Durmaz İncel¹

¹Faculty of EEMCS, Pervasive Systems Research Group, University of Twente, Enschede, The Netherlands.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|April 23, 2025

Summary

This summary is machine-generated.

This study introduces FedOpenHAR, a federated transfer learning framework for multitask human activity recognition (HAR) and device position identification using wearable sensor data. It achieves higher accuracy than centralized training and offers efficient adaptation for new tasks or classes.

Keywords:

federated transfer learning multi-task learning and human activity recognition

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Area of Science:

Computer Science
Machine Learning
Wearable Technology

Background:

Wearable and mobile devices generate valuable user behavior data via motion sensors.
Current analysis often focuses on single tasks like human activity recognition (HAR) using centralized deep learning.
Distributed machine learning offers an alternative, avoiding data transmission to central servers.

Purpose of the Study:

To introduce FedOpenHAR, a federated transfer learning framework for multitask learning.
To enable simultaneous human activity recognition (HAR) and device position identification.
To explore efficient model adaptation for new tasks and classes in a federated setting.

Main Methods:

Utilized the OpenHAR framework with ten datasets for model training.
Employed federated transfer learning with task-specific and personalized layers.
Implemented the DeepConvLSTM architecture within the Flower federated learning environment.

Main Results:

FedOpenHAR achieved 72.4% accuracy, outperforming centralized training (64.5%).
Federated transfer learning performance was comparable to isolated individual training (72.6%).
Demonstrated efficient adaptation for new clients with novel tasks or classes.

Conclusions:

Federated transfer learning in FedOpenHAR enhances multitask analysis of wearable sensor data.
The framework provides a robust and adaptable solution for distributed machine learning on sensor data.
FedOpenHAR offers significant advantages for scalability and incorporating new functionalities in federated learning systems.