Asynchronous and Flexible Federated Learning for COVID-19 Detection from Chest X-Ray Images

抄録

Machine learning, particularly deep learning, is a powerful tool for assisting radiologists in analyzing large volumes of chest X-ray images, significantly accelerating disease diagnosis. However, privacy regulations and data ownership challenges often hinder the centralization of sensitive patient data required for training. Federated learning (FL) addresses these issues by enabling decentralized model training while preserving data confidentiality. This paper introduces a novel FL framework for secure COVID-19 screening using chest X-rays. Our approach incorporates asynchronous communication to overcome delays caused by device heterogeneity and minimizes server-client interactions to reduce network traffic, enhancing scalability. Furthermore, the framework supports heterogeneous client models, ensuring optimized local training. These innovations preserve privacy while achieving performance levels comparable to centralized systems, setting a benchmark for privacy-preserving AI in healthcare.