HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

Abstract

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.

arXiv comment5 pages, 6 figures, Accepted for International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2019 (FL-IJCAI'2019)

References in corpus (3)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• A Kernel Method for the Two-Sample Problem
• Building Ethics into Artificial Intelligence

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