FedGME: Federated Gaussian Mixture-based Learning for Clinical EEG Classification

Federated Learning (FL) enables collaborative model training across hospitals without sharing sensitive local patient data. In clinical EEG, however, label distributions differ strongly across sites, and rare classes, such as uncommon seizure types, may be missing from a hospital altogether. Weight-sharing methods such as FedAvg are costly, scale with model size and are biased toward majority classes under label skew, while prototype-sharing methods such as FedPCL summarise each class by a single point, which collapses the within-class variability intrinsic to EEG and carries little about classes a site has never seen. We propose FedGME (Federated Gaussian Mixture-based Learning for EEG), in which clients exchange low-rank Gaussian components per class instead of weights or prototypes. The server keeps the components of all sites side by side, so each class is shared as a mixture that preserves its spread and can be sampled, allowing clients to learn classes absent from their own data. Built on a pretrained EEG model and evaluated on three corpora from the TUH EEG Corpus, FedGME lets clients recognise classes they never observed, raising their balanced accuracy to 0.15–0.30, where prototype methods stay near zero and FedAvg reaches at most 0.10. On the two multiclass tasks, this improves overall balanced accuracy by 15.5% on average relative to FedAvg (up to +0.060 absolute), while transmitting only 0.2%–1.3% of FedAvg's payload and no model weights. Replacing FedPCL's prototype-wise contrastive loss with an established spread-preserving contrastive loss, weighted by patient identity, prevents the within-class collapse of prototype training and raises effective rank by up to 4.2× over FedPCL at the same payload. On the balanced binary task, where no class is absent, FedGME performs on par with FedAvg. Overall, sharing class distributions instead of model weights offers a communication-efficient strategy for mitigating label skew in federated biomedical signal analysis.