MontageFL: Privacy-Preserving Federated Learning across Heterogeneous EEG Montages A Leakage-Free Benchmark on Alzheimer’s Disease Classification
Abstract
Abstract Background. Federated learning (FL) trains shared models across institutions without moving raw data, which is attractivefor clinical electroencephalography (EEG) where data are scarce and privacy-sensitive. A practical obstacle has receivedlittle attention: clinical sites record EEG with different hardware and electrode montages, so the input feature space itselfdiffers across clients and standard aggregation cannot be applied. A second, quieter problem affects the evidence basefor EEG machine learning. Because a single recording yields thousands of short epochs, splitting epochs (rather thansubjects) between training and test sets leaks subject identity and inflates reported accuracy. This study asks a singlequestion: once heterogeneous montages are harmonized and the evaluation is made leakage-free, what do clinical sitesactually gain from federated EEG learning? Methods. We present MontageFL, a montage-agnostic harmonization pipelinethat maps any electrode configuration to a fixed 88-dimensional feature vector through region-based aggregation, combiningspectral biomarkers (Stream A, 32-d) with covariance-derived features (Stream B, 56-d). The harmonized representation letsa 19-channel site and an 11-channel site train together. We benchmark centralized, local, FedAvg, and FedProx training onAlzheimer’s disease (AD) versus healthy control (HC) classification using the Miltiadous et al. (2023) dataset (65 subjects),under a strictly subject-level evaluation…
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