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

2024-12-27

AI Summary

To address diagnostic capability deficits in low- and middle-income countries—stemming from data scarcity, limited computational resources, and knowledge asymmetry—this paper proposes FedHelp, a cross-institutional federated learning framework. Methodologically, FedHelp integrates federated learning, foundation model collaboration, cross-domain model compression, and joint optimization of medical image classification and segmentation. Its key contributions are: (1) an asymmetric bidirectional knowledge distillation module that explicitly models the inherent asymmetric reciprocity among healthcare institutions; and (2) a lightweight prompt-guided mechanism leveraging a single foundation model API call, drastically reducing communication and computational overhead. Evaluated on multi-center medical imaging tasks, FedHelp improves average client accuracy in under-resourced regions by 12.6% and reduces inference latency by 67%. The framework has been successfully deployed on edge medical devices.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Healthcare Disparity
• Resource Allocation
• Federated Learning Challenge

Innovation

• FedHelp
• Federated Learning
• Medical Resource Allocation