HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
This addresses privacy-preserving EEG classification for applications like human behavior and emotion recognition, but it is incremental as it builds on federated learning with a focus on heterogeneity.
The paper tackles the challenge of training machine learning models on heterogeneous EEG data while preserving privacy, proposing a heterogeneous federated learning approach that achieves consistent performance improvements on real-world tasks.
Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data may vary from subject to subject, may change over time for the same subject, and maybe heterogeneous. Recent years, increasing privacy-preserving demands poses new challenges to this task. The data heterogeneity, as well as the privacy constraint of the EEG data, is not concerned in previous studies. To fill this gap, in this paper, we propose a heterogeneous federated learning approach to train machine learning models over heterogeneous EEG data, while preserving the data privacy of each party. To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices. Our approach achieves consistent performance improvement on every task.