Reputation-Based Federated Learning Defense to Mitigate Threats in EEG
  Signal Classification
Zhibo Zhang Pengfei Li Ahmed Y. Al Hammadi Fusen Guo Ernesto Damiani Chan Yeob Yeun
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.