Pathological speech recognition is challenging because clinical datasets are scarce, variable, and subject to strict privacy constraints preventing cross-institutional data sharing.
These regulations necessitate federated learning (FL) for collaborative training without sharing raw data. However, FL degrades under non-IID data. Hard-clustering FL addresses this by partitioning clients into groups but imposes rigid boundaries, discards boundary samples, and suffers performance drops as cluster numbers increase. We propose Fuzzy Cluster-Based Personalized Federated Learning (FCPFL), using fuzzy C-means to softly group clients and pseudo-label-guided feature selection to identify discriminative features.
FCPFL weights client updates by membership degree, allowing boundary samples to participate in multiple clusters and increasing training data by 25%. Experiments show FCPFL reduces word error rate (WER) by 4.82% and 1.74% on ADReSS and TORGO, compared to hardclustered FL baselines.