  Keyword Spotting (KWS) is a critical aspect of audio-based applications on
mobile devices and virtual assistants. Recent developments in Federated
Learning (FL) have significantly expanded the ability to train machine learning
models by utilizing the computational and private data resources of numerous
distributed devices. However, existing FL methods typically require that
devices possess accurate ground-truth labels, which can be both expensive and
impractical when dealing with local audio data. In this study, we first
demonstrate the effectiveness of Semi-Supervised Federated Learning (SSL) and
FL for KWS. We then extend our investigation to Semi-Supervised Federated
Learning (SSFL) for KWS, where devices possess completely unlabeled data, while
the server has access to a small amount of labeled data. We perform numerical
analyses using state-of-the-art SSL, FL, and SSFL techniques to demonstrate
that the performance of KWS models can be significantly improved by leveraging
the abundant unlabeled heterogeneous data available on devices.
