FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition

TL;DR

This paper presents FED-HARGPT, a hybrid centralized-federated Transformer-based model for human activity recognition that enhances accuracy and privacy preservation on edge devices.

Contribution

It introduces a novel hybrid approach combining centralized and federated learning for HAR using Transformer architecture, evaluated within the Flower framework.

Findings

Federated learning achieves comparable accuracy to centralized models.

The hybrid approach improves robustness in non-IID data scenarios.

Privacy preservation is maintained without significant performance loss.

Abstract

The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from wearable and inertial sensors is generated, facilitating discreet monitoring of human activities, including resting, sleeping, and walking. This research focuses on deploying HAR technologies using mobile sensor data and leveraging Federated Learning within the Flower framework to evaluate the training of a federated model derived from a centralized baseline. The experimental results demonstrate the effectiveness of the proposed hybrid approach in improving the accuracy and robustness of HAR models while preserving data privacy in a non-IID data scenario. The federated learning setup demonstrated comparable performance to centralized models, highlighting the potential of federated learning to strike a balance between data privacy and model performance in real-world applications.