Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis

TL;DR

This paper introduces FedHelp, a federated learning framework that addresses geographic health disparities by enabling knowledge sharing between developed and underserved regions, improving medical diagnosis accuracy in resource-limited settings.

Contribution

The paper proposes a novel asymmetric dual knowledge distillation module and a cross-silo federated learning framework tailored for underserved regions with limited data and resources.

Findings

Significant performance improvements over state-of-the-art methods.

Enhanced diagnostic capabilities in underserved regions.

Effective knowledge transfer between large and small clients.

Abstract

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.