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Federated Transfer Learning for EEG Signal Classification
The success of deep learning (DL) methods in the Brain-Computer Interfac...
04/26/2020 ∙ by Ce Ju, et al. ∙
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Ternary Compression for Communication-Efficient Federated Learning
Learning over massive data stored in different locations is essential in...
03/07/2020 ∙ by Jinjin Xu, et al. ∙
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Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devi...
08/21/2019 ∙ by Tian Li, et al. ∙
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Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Existing traffic flow forecasting approaches by deep learning models ach...
03/19/2020 ∙ by YI LIU, et al. ∙
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Optimal Task Assignment to Heterogeneous Federated Learning Devices
Federated Learning provides new opportunities for training machine learn...
10/01/2020 ∙ by Laércio Lima Pilla, et al. ∙
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Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
In clinical research, the lack of events of interest often necessitates ...
10/18/2019 ∙ by Seok-Ju Hahn, et al. ∙
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GraphFederator: Federated Visual Analysis for Multi-party Graphs
This paper presents GraphFederator, a novel approach to construct joint ...
08/27/2020 ∙ by Dongming Han, et al. ∙
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HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
09/11/2019 ∙ by Dashan Gao, et al. ∙
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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.
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