Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for
  Electroencephalography   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.

30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph