Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis
This work addresses healthcare quality issues in underserved regions of low- and middle-income nations by enhancing collaborative machine learning models, though it is incremental as it builds on existing federated learning methods.
The paper tackled geographic health disparities by proposing FedHelp, a federated learning framework that uses foundational model knowledge and asymmetric dual knowledge distillation to improve diagnostic capabilities in underserved regions, achieving significant performance improvements over state-of-the-art baselines in medical image classification and segmentation tasks.
Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations. Addressing this issue requires a collaborative approach to enhance healthcare quality, leveraging support from medically more developed areas. Federated learning emerges as a promising tool for this purpose. However, the scarcity of medical data and limited computation resources in underserved regions make collaborative training of powerful machine learning models challenging. Furthermore, there exists an asymmetrical reciprocity between underserved and developed regions. To overcome these challenges, we propose a novel cross-silo federated learning framework, named FedHelp, aimed at alleviating geographic health disparities and fortifying the diagnostic capabilities of underserved regions. Specifically, FedHelp leverages foundational model knowledge via one-time API access to guide the learning process of underserved small clients, addressing the challenge of insufficient data. Additionally, we introduce a novel asymmetric dual knowledge distillation module to manage the issue of asymmetric reciprocity, facilitating the exchange of necessary knowledge between developed large clients and underserved small clients. We validate the effectiveness and utility of FedHelp through extensive experiments on both medical image classification and segmentation tasks. The experimental results demonstrate significant performance improvement compared to state-of-the-art baselines, particularly benefiting clients in underserved regions.