Federated Few-shot Learning for Cough Classification with Edge Devices

TL;DR

This paper introduces F2LCough, a novel framework combining federated and few-shot learning to classify cough sounds effectively in data-scarce and privacy-sensitive scenarios, demonstrating promising results on a COVID-19 cough dataset.

Contribution

The paper presents a new framework that integrates federated and few-shot learning for cough classification, addressing data scarcity and privacy concerns.

Findings

Achieved an average F1-Score of 86% on COVID-19 cough dataset.

Demonstrated the feasibility of privacy-preserving cough classification with limited data.

Showed superiority over other methods in data-scarce conditions.

Abstract

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.