Montage-agnostic federated learning for privacy-preserving Alzheimer’s disease classification from heterogeneous multi-site EEG

Federated learning (FL) lets institutions train shared models without moving raw data, attractive for clinical electroencephalography (EEG), where cohorts are small and privacy-sensitive. Two obstacles persist: clinical sites record EEG with different electrode montages, so input feature spaces differ and standard aggregation fails; and because one recording yields thousands of epochs, splitting epochs rather than subjects leaks subject identity and inflates accuracy. We present MontageFL, a montage-agnostic pipeline that maps any electrode configuration to a fixed 88-dimensional feature vector through region-based aggregation of spectral and covariance features, letting a 19-channel and an 11-channel site train together. Benchmarking centralized, local, FedAvg, and FedProx training for Alzheimer’s disease versus healthy-control classification (Miltiadous et al. dataset; 65 subjects) under strictly subject-level, leakage-free evaluation, subject-level accuracy reached approximately 74% (19-channel) and 89% (11-channel), with no detected difference between federated and local models (FedProx versus local, pp; 95% CI ), consistent with parity but unable to exclude a modest effect given the small cohort. Evaluated with epoch-level splitting, the same pipeline scored 15–30 points higher, illustrating leakage-driven inflation. Because montage heterogeneity was simulated by channel subsetting within one acquisition system, validation on independent recording hardware remains future work.