  We propose a novel decentralized feature extraction approach in federated
learning to address privacy-preservation issues for speech recognition. It is
built upon a quantum convolutional neural network (QCNN) composed of a quantum
circuit encoder for feature extraction, and a recurrent neural network (RNN)
based end-to-end acoustic model (AM). To enhance model parameter protection in
a decentralized architecture, an input speech is first up-streamed to a quantum
computing server to extract Mel-spectrogram, and the corresponding
convolutional features are encoded using a quantum circuit algorithm with
random parameters. The encoded features are then down-streamed to the local RNN
model for the final recognition. The proposed decentralized framework takes
advantage of the quantum learning progress to secure models and to avoid
privacy leakage attacks. Testing on the Google Speech Commands Dataset, the
proposed QCNN encoder attains a competitive accuracy of 95.12% in a
decentralized model, which is better than the previous architectures using
centralized RNN models with convolutional features. We also conduct an in-depth
study of different quantum circuit encoder architectures to provide insights
into designing QCNN-based feature extractors. Neural saliency analyses
demonstrate a correlation between the proposed QCNN features, class activation
maps, and input spectrograms. We provide an implementation for future studies.
