摘要
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 by25 % . Experiments show FCPFL reduces word error rate (WER) by𝟒 . 𝟖 𝟐 %and𝟏 . 𝟕 𝟒 %on ADReSS and TORGO, compared to hardclustered FL baselines.