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

Wandemberg Gibaut, Alexandre Osorio, Amparo Munoz, Sildolfo F. G. Neto, Fabio Grassiotto

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.

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

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.

Paper Structure

This paper contains 13 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Theorical Background
  4. Human Activity Recognition
  5. Transformers
  6. Federated Learning
  7. The ExtraSensory dataset
  8. Methodology and Experiments
  9. FED-HARGPT
  10. Centralized Training
  11. Federated Training
  12. Results and Conclusions
  13. Discussion

Figures (3)

  • Figure 1: Logarithmic-scale histogram for one of the cross-validation folds, showing the distribution of six of the labels of the ExtraSensory dataset, illustrates the significant imbalance in label distribution.
  • Figure 2: Box plot for each fold in the Federated Learning process. Note that the dispersion of the clients' BAs has a relatively stable median, but the size of the quartiles and tails of the distributions may present very different values.
  • Figure 3: Histogram of Balanced Accuracy results considering all folds. The blue curve is a Kernel Density Estimate (KDE) that helps visualize the data distribution. Most clients' BAs lie between 0.7 and 0.8, indicating some consistency