Personalised federated learning lets SPDNet EEG decoding beat standard federated and centralised training on three motor-imagery datasets and outperform every EEGNet configuration on two of them
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet, and compares it against standard federated learning and centralised training with the Euclidean EEGNet as a baseline; across three motor-imagery datasets spanning diverse channel, subject and class regimes, personalised SPDNet reaches higher accuracy than both standard federated and centralised training, converges in fewer rounds and communicates fewer parameters than standard federated learning, and outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.