mobile healthcare
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Evaluating Open-Source Large Language Models for Educational Applications
Abstract: Mobile healthcare technologies generate sensitive information through electronic health records, smartphones, wearable sensors, medical imaging systems, and remote monitoring platforms. These distributed data sources can improve disease prediction, continuous monitoring, clinical decision support, and personalized healthcare, but centralized collection creates substantial privacy, security, interoperability, and data-governance concerns. Federated learning (FL) provides a distributed machine learning paradigm in which participating healthcare institutions and personal devices collaboratively train models while retaining raw …
Published in Journal of Open Source Developments · Vol. 13, Issue 2, 2026 · pp. 10–21 Read article