<ul><li>A study explores the use of Federated Learning (FL) for tuberculosis (TB) diagnosis using chest X-rays in low-resource settings across Africa.</li><li>FL allows hospitals to collaboratively train AI models without sharing raw patient data, addressing privacy concerns and data scarcity.</li><li>Challenges in implementing FL in sub-Saharan Africa include poor infrastructure, unreliable internet, limited digital literacy, weak AI regulations, and data control concerns.</li><li>Despite challenges, FL shows potential for AI-driven healthcare in underserved regions, but broader adoption requires improvements in infrastructure, education, and regulatory support.</li></ul>

Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned

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