# HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

arXiv: https://arxiv.org/abs/1909.05784 · HTML: https://mlsanity.com/paper/1909.05784/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images](https://mlsanity.com/paper/2112.10775/) (2021)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [MindBigData 2022 A Large Dataset of Brain Signals](https://mlsanity.com/paper/2212.14746/) (2022)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks](https://mlsanity.com/paper/2306.13109/) (2023)
- [A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets](https://mlsanity.com/paper/2402.10145/) (2024)
- [A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease](https://mlsanity.com/paper/2312.10237/) (2023)
- [Concurrent vertical and horizontal federated learning with fuzzy cognitive maps](https://mlsanity.com/paper/2412.12844/) (2024)
- [Tackling Heterogeneity in Medical Federated learning via Vision Transformers](https://mlsanity.com/paper/2310.09444/) (2023)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques](https://mlsanity.com/paper/2403.11163/) (2024)
- [Federated GNNs for EEG-Based Stroke Assessment](https://mlsanity.com/paper/2411.02286/) (2024)

## Abstract (arXiv)

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.