  Automatically classifying cough sounds is one of the most critical tasks for
the diagnosis and treatment of respiratory diseases. However, collecting a huge
amount of labeled cough dataset is challenging mainly due to high laborious
expenses, data scarcity, and privacy concerns. In this work, our aim is to
develop a framework that can effectively perform cough classification even in
situations when enormous cough data is not available, while also addressing
privacy concerns. Specifically, we formulate a new problem to tackle these
challenges and adopt few-shot learning and federated learning to design a novel
framework, termed F2LCough, for solving the newly formulated problem. We
illustrate the superiority of our method compared with other approaches on
COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average
F1-Score of 86%. Our results show the feasibility of few-shot learning combined
with federated learning to build a classification model of cough sounds. This
new methodology is able to classify cough sounds in data-scarce situations and
maintain privacy properties. The outcomes of this work can be a fundamental
framework for building support systems for the detection and diagnosis of
cough-related diseases.
