Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification. (arXiv:2401.01896v1 [cs.CR])

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 …

aggregation analysis attention brain classification computer defense federated federated learning framework interface mitigation reputation security security threats signal technology threat threat mitigation threats