HHHFL：脑电分层异构水平联邦学习
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
摘要：
脑电（EEG）分类技术已经被广泛研究用于人类行为和情感识别任务。但这仍然是一个具有挑战性的问题，因为数据可能因受试者而异，同一受试者的数据可能会随着时间的推移而变化，而且可能是异构的。近年来，日益增长的隐私保护需求对这项任务提出了新的挑战。在以往的研究中，数据的异质性，以及隐私限制的EEG数据，是不关心的。为了填补这一空白，在本文中，我们提出了一种异构的联邦学习方法，训练机器学习模型在异构EEG数据，同时保持数据的隐私，每一方。为了验证我们的方法的有效性，我们进行实验的真实世界的EEG数据集，从不同的设备收集的异构数据组成。我们的方法在每个任务上都实现了一致的性能改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
