用轻量模型让手机也能参与医疗影像协同训练
Equitable Federated Learning with NCA
- 基于轻量Med-NCA架构，可在手机等低配设备上运行
- 通信开销极低，适配网络不稳的资源匮乏地区
- 支持加密传输，保障数据安全，适合医疗场景
联邦学习（FL）使机构间可在不共享敏感患者数据的前提下协同训练模型，尤其适用于医疗专业人才稀缺的低收入和中等收入国家（LMIC）。然而，这些地区在采用FL时面临严重挑战：高性能计算资源有限且网络连接不可靠。为此，我们提出FedNCA，一种专为医学图像分割任务设计的新型联邦学习系统。该系统利用轻量级Med-NCA架构，可在低成本边缘设备（如普及型智能手机）上进行训练，同时显著降低通信开销。此外，我们的FedNCA具备加密就绪特性，适用于不安全网络环境。通过克服基础设施与安全双重障碍，FedNCA为资源受限地区提供了包容性、高效、轻量且可加密的医疗影像解决方案，推动了公平医疗的发展。
原文摘要 · Abstract (English)
Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and middle-income countries (LMICs), where access to trained medical professionals is limited. However, FL adoption in LMICs faces significant barriers, including limited high-performance computing resources and unreliable internet connectivity. To address these challenges, we introduce FedNCA, a novel FL system tailored for medical image segmentation tasks. FedNCA leverages the lightweight Med-NCA architecture, enabling training on low-cost edge devices, such as widely available smartphones, while minimizing communication costs. Additionally, our encryption-ready FedNCA proves to be suitable for compromised network communication. By overcoming infrastructural and security challenges, FedNCA paves the way for inclusive, efficient, lightweight, and encryption-ready medical imaging solutions, fostering equitable healthcare advancements in resource-constrained regions.
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