用联邦学习在非洲低资源地区联合训练肺结核筛查模型，保护隐私且不依赖集中数据。
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
- 各医院本地训练模型，通过联邦学习聚合更新，避免数据外传。
- 跨八国机构验证，联邦模型性能优于本地模型，证明技术可行性。
- 适合医疗资源匮乏但需共享诊断能力的地区，尤其关注数据安全者。
本研究探讨了在非洲低资源环境中，利用联邦学习（FL）通过胸部X光片进行结核病（TB）诊断的可行性。FL使医院可在不共享原始患者数据的前提下协同训练人工智能模型，缓解隐私顾虑与数据稀缺问题。研究涵盖非洲八个国家的医疗机构与研究中心，多数站点使用本地数据集，加纳与冈比亚则采用公开数据集。通过对比本地训练模型与跨所有机构构建的联邦模型，评估了FL在真实环境中的表现。尽管技术前景广阔，但在撒哈拉以南非洲实施仍面临基础设施差、网络不稳定、数字素养低及缺乏AI监管等问题。部分机构因数据控制担忧，不愿上传模型更新。结论指出，联邦学习在赋能欠发达地区智能医疗方面潜力巨大，但推广需加强基础设施、能力建设与政策支持。
原文摘要 · Abstract (English)
This study explores the use of Federated Learning (FL) for tuberculosis (TB) diagnosis using chest X-rays in low-resource settings across Africa. FL allows hospitals to collaboratively train AI models without sharing raw patient data, addressing privacy concerns and data scarcity that hinder traditional centralized models. The research involved hospitals and research centers in eight African countries. Most sites used local datasets, while Ghana and The Gambia used public ones. The study compared locally trained models with a federated model built across all institutions to evaluate FL's real-world feasibility. Despite its promise, implementing FL in sub-Saharan Africa faces challenges such as poor infrastructure, unreliable internet, limited digital literacy, and weak AI regulations. Some institutions were also reluctant to share model updates due to data control concerns. In conclusion, FL shows strong potential for enabling AI-driven healthcare in underserved regions, but broader adoption will require improvements in infrastructure, education, and regulatory support.
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