Weighted aggregation through probability based ranking: An optimized federated learning architecture to classify respiratory diseases

Abstract

Background and ObjectiveRespiratory Diseases are one of the leading chronic illnesses in the world according to the reports by World Health Organization. Diagnosing these respiratory diseases is done through auscultation where a medical professional listens to sounds of air in the lungs for anomalies through a stethoscope. This method necessitates extensive experience and can also be misinterpreted by the medical professional. To address this issue, we introduce an AI-based solution that listens to the lung sounds and classifies the respiratory disease detected. Since the research work deals with medical data that is tightly under wraps due to privacy concerns in the medical field, we introduce a Deep learning solution to classify the diseases and a custom Federated learning (FL) approach to further improve the accuracy of the deep learning model and simultaneously maintain data privacy. Federated Learning architecture maintains data privacy and facilitates a distributed learning system for medical infrastructures.MethodsThe approach utilizes Generative Adversarial Networks (GAN) based Federated learning approach to ensure data privacy. Generative Adversarial Networks generate new data by synthesizing new lung sounds. This new synthesized data is then converted to spectrograms and trained on a neural network to classify four lung diseases, Heart Attack and Normal breathing patterns. Furthermore, to address performance loss during FL, we also propose a new “Weighted Aggregation through Probability-based Ranking (FedWAPR)” algorithm for optimizing the FL aggregation process. The FedWAPR aggregation takes inspiration from exponential distribution function and ranks better performing clients according to it.Results and ConclusionA test accuracy of about 92% was achieved by the trained model while classifying various respiratory diseases and heart failure. Additionally, we developed a novel FedWAPR approach that significantly outperformed the FedAVG approach for the FL aggregate function. A patient can be checked for respiratory diseases using this improved learning approach without the need for extensive sensitive data recording or for making sure the data sample obtained is secure. In a decentralized training runtime, the trained model successfully classifies various respiratory diseases and heart failure using lung sounds with a test accuracy on par with a centralized model.