Federated Semi-supervised Learning for Industrial Sound Analysis and Keyword Spotting

Obtaining and annotating representative training data for deep learning-based classifiers can be both expensive and impractical in domains such as Industrial Sound Analysis (ISA) and Keyword Spotting (KWS). Furthermore, conventional techniques often rely on centralized servers to store training datasets, raising concerns about data security. We introduce a method that combines Semi-supervised and Federated Learning (FSSL) for classifying audio using Federated Averaging and FixMatch. Our findings indicate that the model’s accuracy decreases by 30 to 50 percentage points when the labeled data is reduced to just 1% of its original volume using standard supervised federated learning. However, our proposed FSSL method improves accuracy by more than 25 percentage points and reaches a nearly perfect accuracy for an ISA dataset, making efficient use of unlabeled data. Furthermore, this FSSL approach proves effective even when data distribution is uneven and clients only label subsets of all target classes.