# Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification

arXiv: https://arxiv.org/abs/2401.01896 · HTML: https://mlsanity.com/paper/2401.01896/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Data Poisoning Attacks on Federated Machine Learning](https://mlsanity.com/paper/2004.10020/) (2020)
- [Federated Transfer Learning for EEG Signal Classification](https://mlsanity.com/paper/2004.12321/) (2020)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [On the Vulnerability of CNN Classifiers in EEG-Based BCIs](https://mlsanity.com/paper/1904.01002/) (2019)
- [Protecting Privacy of Users in Brain-Computer Interface Applications](https://mlsanity.com/paper/1907.01586/) (2019)
- [Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network](https://mlsanity.com/paper/2209.03166/) (2022)
- [Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals](https://mlsanity.com/paper/2301.06923/) (2023)
- [Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies](https://mlsanity.com/paper/1402.5565/) (2014)

## Built on by (most cited first, 5 of 5)

- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Towards SAR Automatic Target Recognition MultiCategory SAR Image Classification Based on Light Weight Vision Transformer](https://mlsanity.com/paper/2407.06128/) (2024)
- [Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence](https://mlsanity.com/paper/2402.07915/) (2024)
- [Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective](https://mlsanity.com/paper/2507.12652/) (2025)
- [Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2511.02797/) (2025)

## Abstract (arXiv)

This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted attention because of the emergence of brain-computer interface (BCI) technology, it is difficult to create efficient learning models for EEG analysis because of the distributed nature of EEG data and related privacy and security concerns. To address these challenges, the proposed defending framework leverages the Federated Learning paradigm to preserve privacy by collaborative model training with localized data from dispersed sources and introduces a reputation-based mechanism to mitigate the influence of data poisoning attacks and identify compromised participants. To assess the efficiency of the proposed reputation-based federated learning defense framework, data poisoning attacks based on the risk level of training data derived by Explainable Artificial Intelligence (XAI) techniques are conducted on both publicly available EEG signal datasets and the self-established EEG signal dataset. Experimental results on the poisoned datasets show that the proposed defense methodology performs well in EEG signal classification while reducing the risks associated with security threats.